Visual and computational analysis of structure-activity relationships in high-throughput screening data

Novel analytic methods are required to assimilate the large volumes of structural and bioassay data generated by combinatorial chemistry and high-throughput screening programmes in the pharmaceutical and agrochemical industries. This paper reviews recent work in visualisation and data mining that can be used to develop structure-activity relationships from such chemical/biological datasets

Deep Metric Learning and Image Classification with Nearest Neighbour Gaussian Kernels

We present a Gaussian kernel loss function and training algorithm for convolutional neural networks that can be directly applied to both distance metric learning and image classification problems. Our method treats all training features from a deep neural network as Gaussian kernel centres and computes loss by summing the influence of a feature's nearby centres in the feature embedding space. Our approach is made scalable by treating it as an approximate nearest neighbour search problem. We show how to make end-to-end learning feasible, resulting in a well formed embedding space, in which semantically related instances are likely to be located near one another, regardless of whether or not the network was trained on those classes. Our approach outperforms state-of-the-art deep metric learning approaches on embedding learning challenges, as well as conventional softmax classification on several datasets.Comment: Accepted in the International Conference on Image Processing (ICIP) 2018. Formerly titled Nearest Neighbour Radial Basis Function Solvers for Deep Neural Network

Hierarchical progressive surveys. Multi-resolution HEALPix data structures for astronomical images, catalogues, and 3-dimensional data cubes

Scientific exploitation of the ever increasing volumes of astronomical data requires efficient and practical methods for data access, visualisation, and analysis. Hierarchical sky tessellation techniques enable a multi-resolution approach to organising data on angular scales from the full sky down to the individual image pixels. Aims. We aim to show that the Hierarchical progressive survey (HiPS) scheme for describing astronomical images, source catalogues, and three-dimensional data cubes is a practical solution to managing large volumes of heterogeneous data and that it enables a new level of scientific interoperability across large collections of data of these different data types. Methods. HiPS uses the HEALPix tessellation of the sphere to define a hierarchical tile and pixel structure to describe and organise astronomical data. HiPS is designed to conserve the scientific properties of the data alongside both visualisation considerations and emphasis on the ease of implementation. We describe the development of HiPS to manage a large number of diverse image surveys, as well as the extension of hierarchical image systems to cube and catalogue data. We demonstrate the interoperability of HiPS and Multi-Order Coverage (MOC) maps and highlight the HiPS mechanism to provide links to the original data. Results. Hierarchical progressive surveys have been generated by various data centres and groups for ~200 data collections including many wide area sky surveys, and archives of pointed observations. These can be accessed and visualised in Aladin, Aladin Lite, and other applications. HiPS provides a basis for further innovations in the use of hierarchical data structures to facilitate the description and statistical analysis of large astronomical data sets.Comment: 21 pages, 6 figures. Accepted for publication in Astronomy & Astrophysic

Overview on spectral line source finding and visualisation

Here I will outline successes and challenges for finding spectral line sources in large data cubes that are dominated by noise. This is a 3D challenge as the sources we wish to catalog are spread over several spatial pixels and spectral channels. While 2D searches can be applied, e.g., channel by channel, optimal searches take into account the 3-dimensional nature of the sources. In this overview I will focus on HI 21-cm spectral line source detection in extragalactic surveys, in particular HIPASS, the "HI Parkes All-Sky Survey" and WALLABY, the "ASKAP HI All-Sky Survey". I use the original HIPASS data to highlight the diversity of spectral signatures of galaxies and gaseous clouds, both in emission and absorption. Among others, I report the discovery of a 680 km/s wide HI absorption trough in the megamaser galaxy NGC 5793. Issues such as source confusion and baseline ripples, typically encountered in single-dish HI surveys, are much reduced in interferometric HI surveys. Several large HI emission and absorption surveys are planned for the Australian Square Kilometre Array Pathfinder (ASKAP): here we focus on WALLABY, the 21-cm survey of the sky (Dec < +30 degr; z < 0.26) which will take about one year of observing time with ASKAP. Novel phased array feeds ("radio cameras") will provide 30 square degrees instantaneous field-of-view. WALLABY is expected to detect more than 500 000 galaxies, unveil their large-scale structures and cosmological parameters, detect their extended, low-surface brightness disks as well as gas streams and filaments between galaxies. It is a precursor for future HI surveys with SKA Phase I and II, exploring galaxy formation and evolution. The compilation of highly reliable and complete source catalogs will require sophisticated source-finding algorithms as well as accurate source parametrisation.Comment: 14 pages, 6 figures, PASA Special Issue on "Source Finding & Visualisation", submitte