Radial velocities for the Hipparcos-Gaia Hundred-Thousand-Proper-Motion project

(abridged) The Hundred-Thousand-Proper-Motion (HTPM) project will determine the proper motions of ~113500 stars using a 23-year baseline. The proper motions will use the Hipparcos data, with epoch 1991.25, as first epoch and the first intermediate-release Gaia astrometry, with epoch ~2014.5, as second epoch. The expected HTPM proper-motion standard errors are 30-190 muas/yr, depending on stellar magnitude. Depending on the characteristics of an object, in particular its distance and velocity, its radial velocity can have a significant impact on the determination of its proper motion. The impact of this perspective acceleration is largest for fast-moving, nearby stars. Our goal is to determine, for each star in the Hipparcos catalogue, the radial-velocity standard error that is required to guarantee a negligible contribution of perspective acceleration to the HTPM proper-motion precision. We employ two evaluation criteria, both based on Monte-Carlo simulations, with which we determine which stars need to be spectroscopically (re-)measured. Both criteria take the Hipparcos measurement errors into account. For each star in the Hipparcos catalogue, we determine the confidence level with which the available radial velocity and its standard error, taken from the XHIP compilation catalogue, are acceptable. We find that for 97 stars, the radial velocities available in the literature are insufficiently precise for a 68.27% confidence level. We also identify 109 stars for which radial velocities are currently unknown yet need to be acquired to meet the 68.27% confidence level. To satisfy the radial-velocity requirements coming from our study will be a daunting task consuming a significant amount of spectroscopic telescope time. Fortunately, the follow-up spectroscopy is not time-critical since the HTPM proper motions can be corrected a posteriori once (improved) radial velocities become available.Comment: Accepted in A&

Session E5: Improving Fish Migration at the Iron Gates I & II Dams

Abstract: In the Danube river basin in Romania, 119 priority fish migration barriers were indicated. In the Danube river basin as a whole, the amount of indicated priority barriers reaches 400 – 500 barriers. The Iron Gate dams were prioritized in the ICPDR Danube river basin management plan as ‘utmost priority’. In 2011, in an assignment for Romania and Serbia (and encouraged by the ICPDR), the FAO conducted a scoping mission to explore general fish migration solutions at the Iron Gates I and II dams. The main conclusion was: provision for migration routes at the Iron Gates dams is technically feasible. Furthermore, the report stated that following steps will have to include the gathering of detailed input from already existing data and from new monitoring activities. The most critical issue will be the choice of the right location of an upstream fish passage entrance: a specific telemetry study on sturgeon is absolutely needed to decide on the location. In 2012 a consortium of Dutch companies and a Romanian research institute (DDNI) started a project subsequent to the FAO scoping mission under the flag of the Dutch partners for water program. The project consisted of a field visit, data collection and analysis (a.o. dam structure, bathymetry, hydrology, ecology), predesign of the most feasible fish migration solutions, cost estimate, stakeholder meeting, international expert meeting and a sturgeon telemetry study (performed by DDNI). Furthermore a roadmap for following steps was made including a planning. This presentation will focus on the main outcomes of the FAO scoping mission and subsequent ‘Partners for water’ project, the project process, key questions and following steps on the roadmap

Extracting Tree-structures in CT data by Tracking Multiple Statistically Ranked Hypotheses

In this work, we adapt a method based on multiple hypothesis tracking (MHT) that has been shown to give state-of-the-art vessel segmentation results in interactive settings, for the purpose of extracting trees. Regularly spaced tubular templates are fit to image data forming local hypotheses. These local hypotheses are used to construct the MHT tree, which is then traversed to make segmentation decisions. However, some critical parameters in this method are scale-dependent and have an adverse effect when tracking structures of varying dimensions. We propose to use statistical ranking of local hypotheses in constructing the MHT tree, which yields a probabilistic interpretation of scores across scales and helps alleviate the scale-dependence of MHT parameters. This enables our method to track trees starting from a single seed point. Our method is evaluated on chest CT data to extract airway trees and coronary arteries. In both cases, we show that our method performs significantly better than the original MHT method.Comment: Accepted for publication at the International Journal of Medical Physics and Practic

Runaway and walkaway stars from the ONC with Gaia DR2

Theory predicts that we should find fast, ejected (runaway) stars of all masses around dense, young star-forming regions. $N$-body simulations show that the number and distribution of these ejected stars could be used to constrain the initial spatial and kinematic substructure of the regions. We search for runaway and slower walkaway stars within 100 pc of the Orion Nebula Cluster (ONC) using $Gaia$ DR2 astrometry and photometry. We compare our findings to predictions for the number and velocity distributions of runaway stars from simulations that we run for 4 Myr with initial conditions tailored to the ONC. In $Gaia$ DR2, we find 31 runaway and 54 walkaway candidates based on proper motion, but not all of these are viable candidates in three dimensions. About 40 per cent are missing radial velocities, but we can trace back 9 3D-runaways and 24 3D-walkaways to the ONC, all of which are low/intermediate-mass (<8 M$_{\odot}$). Our simulations show that the number of runaways within 100 pc decreases the older a region is (as they quickly travel beyond this boundary), whereas the number of walkaways increases up to 3 Myr. We find fewer walkaways in $Gaia$ DR2 than the maximum suggested from our simulations, which may be due to observational incompleteness. However, the number of $Gaia$ DR2 runaways agrees with the number from our simulations during an age of $\sim$1.3-2.4 Myr, allowing us to confirm existing age estimates for the ONC (and potentially other star-forming regions) using runaway stars.Comment: 19 pages, 7 figures, accepted for publication in MNRA

Machine learning approaches in medical image analysis: From detection to diagnosis

Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging: coping with variation in imaging protocols, learning from weak labels, and interpretation and evaluation of results