Collectively coping with contact: The role of intragroup support in dealing with the challenges of intergroup mixing in residential contexts

The social identity approach to stress has shown how intragroup support processes shape individuals' responses to stress across health care, workplace, and community settings. However, the issue of how these 'social cure' processes can help cope with the stress of intergroup contact has yet to be explored. This is particularly important given the pivotal role of intergroup threat and anxiety in the experience of contact as well as the effect of contact on extending the boundaries of group inclusion. This study applies this perspective to a real-life instance of residential contact in a divided society. Semi-structured interviews with 14 Catholic and 13 Protestant new residents of increasingly mixed areas of Belfast city, Northern Ireland, were thematically analysed. Results highlight that transitioning to mixed communities was fraught with intergroup anxiety, especially for those coming from 'single identity' areas. Help from existing residents, especially when offered by members of other religious denominations, signalled a 'mixed community ethos' to new residents, which facilitated adopting and sharing this identity. This shared identity then enabled them to deal with unexpected intergroup threats and provided resilience to future sectarian division. New residents who did not adopt this shared identity remained isolated, fearful, and prone to negative contact.

Building Support Vector Machines in the Context of Regularised Least Squares

This paper formulates a linear kernel support vector machine (SVM) as a regularized least-squares (RLS) problem. By defining a set of indicator variables of the errors, the solution to the RLS problem is represented as an equation that relates the error vector to the indicator variables. Through partitioning the training set, the SVM weights and bias are expressed analytically using the support vectors. It is also shown how this approach naturally extends to Sums with nonlinear kernels whilst avoiding the need to make use of Lagrange multipliers and duality theory. A fast iterative solution algorithm based on Cholesky decomposition with permutation of the support vectors is suggested as a solution method. The properties of our SVM formulation are analyzed and compared with standard SVMs using a simple example that can be illustrated graphically. The correctness and behavior of our solution (merely derived in the primal context of RLS) is demonstrated using a set of public benchmarking problems for both linear and nonlinear SVMs.