There is a growing need for abstractions in logic specification languages such as FO(·) and ASP. One technique to achieve these abstractions are templates (sometimes called macros). While the semantics of templates are virtually always described through a syntactical rewriting scheme, we present an alternative view on templates as second order definitions. To extend the existing definition cons...

There are many situations in which we have more than one view of a single data source, or in which we have multiple sources of data that are aligned. We would like to be able to build classifiers which incorporate these to enhance classification performance. Kernel Fisher Discriminant Analysis (KFDA) can be formulated as a convex optimisation problem, which we extend to the Multiview setting (M...

This paper discusses our experiences regarding the design and implementation of the MultiView system { a framework for the speciication, creation, and management of updatable views in object-oriented databases (OODBs) { using commercially available OODB technology. MultiView, which simpliies view schema design and maintenance by breaking view speciication into the tasks of class derivation, glo...

Multi-view learning leverages correlations between different sources of data to make predictions in one view based on observations in another view. A popular approach is to assume that, both, the correlations between the views and the view-specific covariances have a lowrank structure, leading to inter-battery factor analysis, a model closely related to canonical correlation analysis. We propos...

We propose an approach to multi-view object detection and pose estimation that considers combinations of single-view estimates. It can be used with most existing single-view pose estimation systems, and can produce improved results even if the individual pose estimates are incoherent. The method is introduced in the context of an existing, probabilistic, view-based detection and pose estimation...

Multi-view learners reduce the need for labeled data by exploiting disjoint sub-sets of features (views), each of which is sufficient for learning. Such algorithms assume that each view is a strong view (i.e., perfect learning is possible in each view). We extend the multi-view framework by introducing a novel algorithm, Aggressive Co-Testing, that exploits both strong and weak views; in a weak...

This research reviews and tests two contradicting notions in cost stickiness literature by empirical recognition of the consequences of cost stickiness. Cost stickiness is consistent with both rational resource planning and opportunistic incentives of manager to increase personal benefits arising from status and power. Although both mechanisms involve asymmetric retention of slack, some of the ...