Development of teaching resources for physical modelling community

Physical modelling of interesting geotechnical problems has helped clarify behaviours and failure mechanisms of many civil engineering systems. Interesting visual information from physical modelling can also be used in teaching to foster interest in geotechnical engineering and recruit young researchers to our field. With this intention, the Teaching Committee of TC2 developed a web-based teaching resources centre. In this paper, the development and organisation of the resource centre using Wordpress. Wordpress is an open-source content management system which allows user content to be edited and site administration to be controlled remotely via a built-in interface. Example data from a centrifuge test on shallow foundations which could be used for undergraduate or graduate level courses is presented and its use illustrated. A discussion on the development of wiki-style addition to the resource centre for commonly used physical model terms is also presented. © 2010 Taylor & Francis Group, London.

Semantic transform: Weakly supervised semantic inference for relating visual attributes

Relative (comparative) attributes are promising for thematic ranking of visual entities, which also aids in recognition tasks. However, attribute rank learning often requires a substantial amount of relational supervision, which is highly tedious, and apparently impractical for real-world applications. In this paper, we introduce the Semantic Transform, which under minimal supervision, adaptively finds a semantic feature space along with a class ordering that is related in the best possible way. Such a semantic space is found for every attribute category. To relate the classes under weak supervision, the class ordering needs to be refined according to a cost function in an iterative procedure. This problem is ideally NP-hard, and we thus propose a constrained search tree formulation for the same. Driven by the adaptive semantic feature space representation, our model achieves the best results to date for all of the tasks of relative, absolute and zero-shot classification on two popular datasets. © 2013 IEEE.