Berichte über die Tätigkeit von Untersuchungsämtern usw.

European Food Research and Technology -     

Referate

Ohne Zusammenfassung     

Referate

Ohne Zusammenfassung     

Effects of organizational scheme and labeling on task performance in product-centered and user-centered retail Web sites

As companies increase the quantity of information they provide through their Web sites, it is critical that content is structured with an appropriate architecture. However, resource constraints often limit the ability of companies to apply all Web design principles completely. This study quantifies the effects of two major information architecture principles in a controlled study that isolates the incremental effects of organizational scheme and labeling on user performance and satisfaction. Sixty participants with a wide range of Internet and on-line shopping experience were recruited to complete a series of shopping tasks on a prototype retail shopping Web site. User-centered labels provided a significant benefit in performance and satisfaction over labels obtained through company-centered methods. User-centered organization did not result in improved performance except when the label quality was poor. Significant interactions suggest specific guidelines for allocating resources in Web site design. Applications of this research include the design of Web sites for any commercial application, particularly E-commerce.     

A mathematical analysis of the effects of Hebbian learning rules on the dynamics and structure of discrete-time random recurrent neural networks

We present a mathematical analysis of the effects of Hebbian learning in random recurrent neural networks, with a generic Hebbian learning rule, including passive forgetting and different timescales, for neuronal activity and learning dynamics. Previous numerical work has reported that Hebbian learning drives the system from chaos to a steady state through a sequence of bifurcations. Here, we interpret these results mathematically and show that these effects, involving a complex coupling between neuronal dynamics and synaptic graph structure, can be analyzed using Jacobian matrices, which introduce both a structural and a dynamical point of view on neural network evolution. Furthermore, we show that sensitivity to a learned pattern is maximal when the largest Lyapunov exponent is close to 0. We discuss how neural networks may take advantage of this regime of high functional interest.