One of the fundamental problems in computational structural biology is the prediction of RNA secondary structures from a single sequence. To solve this problem, mainly two different approaches have been used over the past decades: the free energy minimization (MFE) approach which is still considered the most popular and successful method and the competing stochastic context-free grammar (SCFG) approach. While the accuracy of the MFE based algorithms is limited by the quality of underlying thermodynamic models, the SCFG method abstracts from free energies and instead tries to learn about the structural behavior of the molecules by training the grammars on known real RNA structures, making it highly dependent on the availability of a rich high quality training set. However, due to the respective problems associated with both methods, new statistics based approaches towards RNA structure prediction have become increasingly appreciated. For instance, over the last years, several statistical sampling methods and clustering techniques have been invented that are based on the computation of partition functions (PFs) and base pair probabilities according to thermodynamic models. A corresponding SCFG based statistical sampling algorithm for RNA secondary structures has been studied just recently. Notably, this probabilistic method is capable of producing accurate (prediction) results, where its worst-case time and space requirements are equal to those of common RNA folding algorithms for single sequences.
The aim of this work is to present a comprehensive study on how enriching the underlying SCFG by additional information on the lengths of generated substructures (i.e. by incorporating length-dependencies into the SCFG based sampling algorithm, which is actually possible without significant losses in performance) affects the reliability of the induced RNA model and the accuracy of sampled secondary structures. As we will see, significant differences with respect to the overall quality of generated sample sets and the resulting predictive accuracy are typically implied. In principle, when considering the more specialized length-dependent SCFG model as basis for statistical sampling, a higher accuracy of predicted foldings can be reached at the price of a lower diversity of generated candidate structures (compared to the more general traditional SCFG variant or sampling based on PFs that rely on free energies).
SHIM is a concurrent deterministic programming language for embedded systems built on rendezvous communication. It abstracts away many details to give the developer a high-level view that includes virtual shared variables, threads as orthogonal statements, and deterministic concurrent exceptions.
In this paper, we present a new way to compile a SHIM-like language into a set of asynchronous guarded actions, a well-established intermediate representation for concurrent systems. By doing so, we build a bridge to many other tools, including hardware synthesis and formal verification. We present our translation in detail, illustrate it through examples, and show how the result can be used by various other tools.
This report gives an overview of the separate translation of synchronous imperative programs to synchronous guarded actions. In particular, we consider problems to be solved for separate compilation that stem from preemption statements and local variable declarations. We explain how we solved these problems and sketch our solutions implemented in the our Averest framework to implement a compiler that allows a separate compilation of imperative synchronous programs with local variables and unrestricted preemption statements. The focus of the report is the big picture of our entire design flow.
In this article we present a method to generate random objects from a large variety of combinatorial classes according to a given distribution. Given a description of the combinatorial class and a set of sample data our method will provide an algorithm that generates objects of size n in worst-case runtime O(n^2) (O(n log(n)) can be achieved at the cost of a higher average-case runtime), with the generated objects following a distribution that closely matches the distribution of the sample data.
Das Handlungsreisendenproblem ist eines der bekanntesten NP-vollständigen kombinatorischen Optimierungsprobleme. Algorithmen mit unterschiedlichen Konzepten und Strategien wurden entwickelt, die sich in Lösungsqualität und Laufzeit unterscheiden. Ein vergleichsweise neuer Ansatz stellt jedoch das Kombinieren vorhandener Lösungen dar. Hier werden die Kantenmengen gegebener Handlungsreisendertouren als Ausgangsbasis für weitere Optimierungen verwendet. Erste Veröffentlichungen zu diesem Gebiet stellen bereits vielversprechende Ergebnisse vor, die nachfolgend vertieft evaluiert werden.
The Chained Lin-Kernighan algorithm (CLK) is one of the best heuristics to solve Traveling Salesman Problems (TSP). In this paper a distributed algorithm is proposed, were nodes in a network locally optimize TSP instances by using the CLK algorithm. Within an Evolutionary Algorithm (EA) network-based framework the resulting tours are modified and exchanged with neighboring nodes. We show that the distributed variant finds better tours compared to the original CLK given the same amount of computation time. For instance fl3795, the original CLK got stuck in local optima in each of 10 runs, whereas the distributed algorithm found optimal tours in each run requiring less than 10 CPU minutes per node on average in an 8 node setup. For instance sw24978, the distributed algorithm had an average solution quality of 0.050% above the optimum, compared to CLK's average solution of 0.119% above the optimum given the same total CPU time (104 seconds). Considering the best tours of both variants for this instance, the distributed algorithm is 0.033% above the optimum and the CLK algorithm 0.099%.
The provision of quality-of-service (QoS) on the network layer is a major challenge in communication networks. This applies particularly to mobile ad-hoc networks (MANETs) in the area of Ambient Intelligence (AmI), especially with the increasing use of delay and bandwidth sensitive applications. The focus of this survey lies on the classification and analysis of selected QoS routing protocols in the domain of mobile ad-hoc networks. Each protocol is briefly described and assessed, and the results are summarized in multiple tables.
This article focuses on the analytical analysis of the free energy in a realistic model for RNA secondary structures. In fact, the free energy in a stochastic model derived from a database of small and large subunit ribosomal RNA (SSU and LSU rRNA) data is studied. A common thermody-namic model for computing the free energy of a given RNA secondary structure, as well as stochastic context-free grammars and generating functions are used to derive the desired results. These results include asymptotics for the expected free energy and for the corresponding variance of a random RNA secondary structure. The quality of our model is judged by comparing the derived results to the used database of SSU and LSU rRNA data. At the end of this article, it is discussed how our results could be used to help on identifying good predictions of RNA secondary structure.