The diversity and complexity of settings and arrangements forming the ‘experienced environments’ for doctoral candidates : some implications for doctoral education

A significant feature of contemporary doctoral education is the continuing trend for research and research education to migrate beyond discipline-based institutional teaching and research structures. The result is a more diverse array of settings and arrangements for doctoral education linked to an increasingly global research enterprise. Recognising the complexity of what is a distributed environment challenges some commonly held assumptions about doctoral education and its practice. Drawing on data gathered in an Australian study of PhD programme development in Australia carried out in 2006–2009, the article describes the fluid and complex arrangements forming the ‘experienced environments’ for doctoral candidates, an environment that can afford them varying opportunities and challenges for completing their candidacy. Some implications for doctoral education are discussed.

Quality-based SQL: specifying information quality in relational database queries

A Structured Query Language extension uses an estimator module to evaluate quality profiles that rate the accuracy and completeness of query results. Users receive information that matches their defined quality constraints and better serves their data needs.

Collaborating differently on different topics: a multi-relational approach to multi-task learning

Multi-task learning offers a way to benefit from synergy of multiple related prediction tasks via their joint modeling. Current multi-task techniques model related tasks jointly, assuming that the tasks share the same relationship across features uniformly. This assumption is seldom true as tasks may be related across some features but not others. Addressing this problem, we propose a new multi-task learning model that learns separate task relationships along different features. This added flexibility allows our model to have a finer and differential level of control in joint modeling of tasks along different features. We formulate the model as an optimization problem and provide an efficient, iterative solution. We illustrate the behavior of the proposed model using a synthetic dataset where we induce varied feature-dependent task relationships: positive relationship, negative relationship, no relationship. Using four real datasets, we evaluate the effectiveness of the proposed model for many multi-task regression and classification problems, and demonstrate its superiority over other state-of-the-art multi-task learning models