Reducing the cost of applying adaptive test cases

The testing of a state-based system may involve the application of a number of adaptive test cases. Where the implementation under test (IUT) is deterministic, the response of the IUT to some adaptive test case $\gamma_1$ could be capable of determining the response of the IUT to another adaptive test case $\gamma_2". Thus, the expected cost of applying a set of adaptive test cases depends upon the order in which they are applied. This paper explores properties of adaptive test cases and considers the problem of finding an order of application of the elements from some set of adaptive test cases, which minimises the expected cost of testing

Adaptive response and enlargement of dynamic range

Many membrane channels and receptors exhibit adaptive, or desensitized, response to a strong sustained input stimulus, often supported by protein activity-dependent inactivation. Adaptive response is thought to be related to various cellular functions such as homeostasis and enlargement of dynamic range by background compensation. Here we study the quantitative relation between adaptive response and background compensation within a modeling framework. We show that any particular type of adaptive response is neither sufficient nor necessary for adaptive enlargement of dynamic range. In particular a precise adaptive response, where system activity is maintained at a constant level at steady state, does not ensure a large dynamic range neither in input signal nor in system output. A general mechanism for input dynamic range enlargement can come about from the activity-dependent modulation of protein responsiveness by multiple biochemical modification, regardless of the type of adaptive response it induces. Therefore hierarchical biochemical processes such as methylation and phosphorylation are natural candidates to induce this property in signaling systems.Comment: Corrected typos, minor text revision

Adaptive Alternating Minimization Algorithms

The classical alternating minimization (or projection) algorithm has been successful in the context of solving optimization problems over two variables. The iterative nature and simplicity of the algorithm has led to its application to many areas such as signal processing, information theory, control, and finance. A general set of sufficient conditions for the convergence and correctness of the algorithm is quite well-known when the underlying problem parameters are fixed. In many practical situations, however, the underlying problem parameters are changing over time, and the use of an adaptive algorithm is more appropriate. In this paper, we study such an adaptive version of the alternating minimization algorithm. As a main result of this paper, we provide a general set of sufficient conditions for the convergence and correctness of the adaptive algorithm. Perhaps surprisingly, these conditions seem to be the minimal ones one would expect in such an adaptive setting. We present applications of our results to adaptive decomposition of mixtures, adaptive log-optimal portfolio selection, and adaptive filter design.Comment: 12 pages, to appear in IEEE Transactions on Information Theor

Model-driven transformation and validation of adaptive educational hypermedia using CAVIAr

Authoring of Adaptive Educational Hypermedia is a complex activity requiring the combination of a range of design and validation techniques.We demonstrate how Adaptive Educational Hypermedia can be transformed into CAVIAr courseware validation models allowing for its validation. The model-based representation and analysis of different concerns and model-based mappings and transformations are key contributors to this integrated solution. We illustrate the benefits of Model Driven Engineering methodologies that allow for interoperability between CAVIAr and a well known Adaptive Educational Hypermedia framework. By allowing for the validation of Adaptive Educational Hypermedia, the course creator limits the risk of pedagogical problems in migrating to Adaptive Educational Hypermedia from static courseware