Sublinear Algorithms for Approximating String Compressibility

We raise the question of approximating the compressibility of a string with respect to a fixed compression scheme, in sublinear time. We study this question in detail for two popular lossless compression schemes: run-length encoding (RLE) and a variant of Lempel-Ziv (LZ77), and present sublinear algorithms for approximating compressibility with respect to both schemes. We also give several lower bounds that show that our algorithms for both schemes cannot be improved significantly. Our investigation of LZ77 yields results whose interest goes beyond the initial questions we set out to study. In particular, we prove combinatorial structural lemmas that relate the compressibility of a string with respect to LZ77 to the number of distinct short substrings contained in it (its ?th subword complexity , for small ?). In addition, we show that approximating the compressibility with respect to LZ77 is related to approximating the support size of a distribution.     

Two-phase flow in a horizontal rectangular microchannel

Two-phase flow in a rectangular short horizontal channel 200 μm high was studied experimentally. The use of the fluorescent method made it possible to reveal flow of liquid in the channel and to determine its characteristics quantitatively. The existence of the regime of separate (stratified) flow is established. Based on analysis of previous investigations and newly obtained data, it is shown that a change in the height of the horizontal channel has a substantial effect on the boundaries between the regimes. The region of the churn regime increases with decreasing thickness of the channel.     

Why is a computational framework for motivational and metacognitive control needed?

This paper discusses, in the context of computational modelling and simulation of cognition, the relevance of deeper structures in the control of behaviour. Such deeper structures include motivational control of behaviour, which provides underlying causes for actions, and also metacognitive control, which provides higher-order processes for monitoring and regulation. It is argued that such deeper structures are important and thus cannot be ignored in computational cognitive architectures. A general framework based on the Clarion cognitive architecture is outlined that emphasises the interaction amongst action selection, motivation, and metacognition. The upshot is that it is necessary to incorporate all essential processes; short of that, the understanding of cognition can only be incomplete.     

A structured overview on the use of computational simulators for teaching statistical methods

Modern technology is affecting what is taught and how statistics is taught. Computer intensive methods like bootstrapping, generalized regression, coordinate exchange algorithms and computer experiment emulators are now part of the statistical toolkit. In parallel with these new methods and tools, a similar evolution appears in statistical education. Simulators offer opportunities for hands-on experience in the classroom, bridging the gap between theory and practice. In this article, we map a wide range of simulation based tools used in teaching statistical methods, putting them in the context of a quality ladder representing various maturity levels of organizations and course level sophistication. The mapping is designed to help educators assess and integrate options related to the incorporation of simulators as teaching aids in an educational and pedagogical roadmap. The mapping is applied to specific simulation products but provides a general framework that can be expanded and used in a generic way.