VALUING COMMUNITY DEVELOPMENT THROUGH THE SOCIAL INCLUSION PROGRAMME (SICAP) 2015–2017 TOWARDS A FRAMEWORK FOR EVALUATION. ESRI RESEARCH SERIES NUMBER 77 FEBRUARY 2019

The Social Inclusion and Community Activation Programme (SICAP) represents a major component of Ireland’s community development strategy, led by the Department of Rural and Community Development (DRCD). The vision of SICAP is to improve the opportunities and life chances of those who are marginalised in society, experiencing unemployment or living in poverty through community development approaches, targeted supports and interagency collaboration, where the values of equality and inclusion are promoted and human rights are respected. In 2016, total expenditure on SICAP amounted to approximately €36 million (Pobal, 2016a). Using a mixed methodology, this report examines the extent to which community development programmes can or should be subject to evaluation, with a particular focus on SICAP. In doing so, the report draws on a rich body of information – including desk-based research; consultation workshops with members of local community groups (LCGs), local community workers (LCWs) and other key policy stakeholders; and an analysis of administrative data held by Pobal – on the characteristics of LCGs that received direct support under SICAP. The findings in this report relate to the delivery of the SICAP 2015–2017 programme which ended in December 2017. The aim of the study is to inform policy by shedding light on a number of issues including the following. Can community development be evaluated? What are the current metrics and methodologies suggested in the literature for evaluating community development interventions? What possible metrics can be used to evaluate community development interventions and how do these relate to the SICAP programme? How can a framework be developed that could potentially be used by SICAP for monitoring evaluation of its community development programme

Distributed-memory large deformation diffeomorphic 3D image registration

We present a parallel distributed-memory algorithm for large deformation diffeomorphic registration of volumetric images that produces large isochoric deformations (locally volume preserving). Image registration is a key technology in medical image analysis. Our algorithm uses a partial differential equation constrained optimal control formulation. Finding the optimal deformation map requires the solution of a highly nonlinear problem that involves pseudo-differential operators, biharmonic operators, and pure advection operators both forward and back- ward in time. A key issue is the time to solution, which poses the demand for efficient optimization methods as well as an effective utilization of high performance computing resources. To address this problem we use a preconditioned, inexact, Gauss-Newton- Krylov solver. Our algorithm integrates several components: a spectral discretization in space, a semi-Lagrangian formulation in time, analytic adjoints, different regularization functionals (including volume-preserving ones), a spectral preconditioner, a highly optimized distributed Fast Fourier Transform, and a cubic interpolation scheme for the semi-Lagrangian time-stepping. We demonstrate the scalability of our algorithm on images with resolution of up to $1024^3$ on the "Maverick" and "Stampede" systems at the Texas Advanced Computing Center (TACC). The critical problem in the medical imaging application domain is strong scaling, that is, solving registration problems of a moderate size of $256^3$---a typical resolution for medical images. We are able to solve the registration problem for images of this size in less than five seconds on 64 x86 nodes of TACC's "Maverick" system.Comment: accepted for publication at SC16 in Salt Lake City, Utah, USA; November 201