HelloWorld! An Instructive Case for the Transformation Tool Contest

This case comprises several primitive tasks that can be solved straight away with most transformation tools. The aim is to cover the most important kinds of primitive operations on models, i.e. create, read, update and delete (CRUD). To this end, tasks such as a constant transformation, a model-to-text transformation, a very basic migration transformation or diverse simple queries or in-place operations on graphs have to be solved. The motivation for this case is that the results expectedly will be very instructive for beginners. Also, it is really hard to compare transformation languages along complex cases, because the complexity of the respective case might hide the basic language concepts and constructs.Comment: In Proceedings TTC 2011, arXiv:1111.440

A circuit topology approach to categorizing changes in biomolecular structure

The biological world is composed of folded linear molecules of bewildering topological complexity and diversity. The topology of folded biomolecules such as proteins and ribonucleic acids is often subject to change during biological processes. Despite intense research, we lack a solid mathematical framework that summarizes these operations in a principled manner. Circuit topology, which formalizes the arrangements of intramolecular contacts, serves as a general mathematical framework to analyze the topological characteristics of folded linear molecules. In this work, we translate familiar molecular operations in biology, such as duplication, permutation, and elimination of contacts, into the language of circuit topology. We show that for such operations there are corresponding matrix representations as well as basic rules that serve as a foundation for understanding these operations within the context of a coherent algebraic framework. We present several biological examples and provide a simple computational framework for creating and analyzing the circuit diagrams of proteins and nucleic acids. We expect our study and future developments in this direction to facilitate a deeper understanding of natural molecular processes and to provide guidance to engineers for generating complex polymeric materials

Wavelet-based estimation with multiple sampling rates

We suggest an adaptive sampling rule for obtaining information from noisy signals using wavelet methods. The technique involves increasing the sampling rate when relatively high-frequency terms are incorporated into the wavelet estimator, and decreasing it when, again using thresholded terms as an empirical guide, signal complexity is judged to have decreased. Through sampling in this way the algorithm is able to accurately recover relatively complex signals without increasing the long-run average expense of sampling. It achieves this level of performance by exploiting the opportunities for near-real time sampling that are available if one uses a relatively high primary resolution level when constructing the basic wavelet estimator. In the practical problems that motivate the work, where signal to noise ratio is particularly high and the long-run average sampling rate may be several hundred thousand operations per second, high primary resolution levels are quite feasible.Comment: Published at http://dx.doi.org/10.1214/009053604000000751 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

GENETIC ALGORITHM CONTROLLED COMMON SUBEXPRESSION ELIMINATION FOR SPILL-FREE REGISTER ALLOCATION

As code complexity increases, maxlive increases. This is especially true in the case of the Kentucky If-Then-Else architecture proposed for Nanocontrollers. To achieve low circuit complexity, computations are decomposed to bit-level operations, thus generating large blocks of code with complex dependence structures. Additionally, the Nanocontroller architecture allows for only a small number of single bit registers and no extra memory. The assumption of an infinite number of registers made during code generation becomes a huge problem during register allocation because the small number of registers and no additional memory. The large basic blocks mean that maxlive almost always exceeds the number of registers and the traditional methods of register allocation such as instruction re-ordering and register spill/reload cannot be applied trivially. This thesis deals with finding a solution to reduce maxlive for successful register allocation using Genetic Algorithms

Analysis of Memory Latencies in Multi-Processor Systems

Predicting timing behavior is key to efficient embedded real-time system design and verification. Current approaches to determine end-to-end latencies in parallel heterogeneous architectures focus on performance analysis either on task or system level. Especially memory accesses, basic operations of embedded application, cannot be accurately captured on a single level alone: While task level methods simplify system behavior, system level methods simplify task behavior. Both perspectives lead to overly pessimistic estimations. To tackle these complex interactions we integrate task and system level analysis. Each analysis level is provided with the necessary data to allow precise computations, while adequate abstraction prevents high time complexity