Computing Laboratory

Differences in mRNA expression levels have been observed in failing versus non-failing human hearts for several membrane channel proteins and accessory subunits. These differences may play a causal role in electrophysiological changes observed in human heart failure and atrial fibrillation, such as action potential (AP) prolongation, increased AP triangulation, decreased intracellular calcium transient (CaT) magnitude and decreased CaT triangulation. Our goal is to investigate whether the information contained in mRNA measurements can be used to predict cardiac electrophysiological remodeling in heart failure using computational modeling. Using mRNA data recently obtained from failing and non-failing human hearts, we construct failing and non-failing cell populations incorporating natural variability and up/down regulation of channel conductivities. Six biomarkers are calculated for each cell in each population, at cycle lengths between 1500 ms and 300 ms. Regression analysis is performed to determine which ion channels drive biomarker variability in failing versus nonfailing cardiomyocytes. Our models suggest that reported mRNA expression changes are consistent with AP prolongation, increased AP triangulation, increased CaT duration, decreased CaT triangulation and amplitude, and increased delay between AP and CaT upstrokes in the failing population. Regression analysis reveals that changes in AP biomarkers are driven primarily by reduction in I Kr , and changes in CaT biomarkers are driven predominantly by reduction in I CaL and SERCA. In particular, the role of I CaL is pacing rate dependent. Additionally, alternans developed at fast pacing rates for both failing and non-failing cardiomyocytes, but the underlying mechanisms are different in control and heart failure.

Ontologies and rules play a central role in the development of the Semantic Web. Recent research in this context focuses especially on highly scalable formalisms for the Web of Data, which may highly benefit from exploiting database technologies. In this paper, as a first step towards closing the gap between the Semantic Web and databases, we introduce a family of expressive extensions of Datalog, called Datalog±, as a new paradigm for query answering over ontologies. The Datalog± family admits existentially quantified variables in rule heads, and has suitable restrictions to ensure highly efficient ontology querying. We show in particular that different versions of Datalog± generalize the tractable description logic ℇℒ and the DL-Lite family of tractable description logics, which are the most common tractable ontology languages in the context of the Semantic Web and databases. We also show how stratified negation can be added to Datalog± while keeping ontology querying tractable. Furthermore, the Datalog± family is of interest in its own right, and can, moreover, be used in various contexts such as data integration and data exchange. It paves the way for applying results from databases to the context of the Semantic Web.

We review the concepts of hypertree decomposition and hypertree width from a graph theoretical perspective and report on a number of recent results related to these concepts. We also showas a new result-that computing hypertree decompositions is fixed-parameter intractable. 1 Hypertree Decompositions: Definition and Basics This paper reports about the recently introduced concept of hypertree decomposition and the associated notion of hypertree width. The latter is a cyclicity measure for hypergraphs, and constitutes a hypergraph invariant as it is preserved under hypergraph isomorphisms. Many interesting NP-hard problems are polynomially solvable for classes of instances are associated with hypergraphs of bounded width. This is also true for other hypergraph invariants such as treewidth, cutset-width, and so on. However, the advantage of hypertree width with respect to other known hypergraph invariants is that it is more general and covers larger classes of instances of bounded width. The main concepts of hypertree decomposition and hypertree width are introduced in the present section. A normal form for hypertree decompositions is described in Section 2. Section 3 describes the Robbers and Marshals game which caracterizes hypertree-width. In Section 4 we use this game to explain why the problem of checking whether the hypertree width of a hypergraph is ≤ k is feasible in polynopmial time for each constant k. However, in Section 5 we show that this problem is fixed-parameter intractable with respect to k. In Section 6 we compare hypertree width to other relevant hypergraph invariants. In Section 7 we discuss heuristics for computing hypertree decompositions. In Section 8 we show how hypertree decompositions can be beneficially applied for solving constraint satisfaction problems (CSPs). Finally, in Section 9 we list some open problems left for future research. Due to space limitations this paper is rather short, and most proofs are missing. A more thorough treatment of can be found in [14,17,2,1,16,18], most of which are available at the Hypertree Decompositions Homepage at http://si.deis.unical.it/ frank/Hypertrees. A hypergraph is a pair H = (V (H), E(H)), consisting of a nonempty set V (H) of vertices, and a set E(H) of subsets of V (H), the hyperedges of H. We only consider finite hypergraphs. Graphs are hypergraphs in which all hyperedges have two elements. For a hypergraph H and a set X ⊆ V (H), the subhypergraph induced by X is the hypergraph H[X] = (X, {e ∩ X | e ∈ E(H)}). We let H \ X := H[V (H) \ X]. The primal graph of a hypergraph H is the graph H = (V (H), {{v, w} | v = w, there exists an e ∈ E(H) such that {v, w} ⊆ e}). A hypergraph H is connected if H is connected. A set C ⊆ V (H) is connected (in H) if the induced subhypergraph H[C] is connected, and a connected component of H is a maximal connected subset of V (H). A sequence of nodes of V (H) is a path of H if it is a path of H. A tree decomposition of a hypergraph H is a tuple (T, χ), where T = (V (T), E(T)) is a tree and χ : V (T) −→ 2 V (H) is a function associating a set of vertices χ(t) ⊆ V (H) to each vertex t of the This paper was supported by the Austrian Science Fund (FWF) project: Nr. P17222-N04, Complementary Approaches to Constraint Satisfaction.

RoboCup has been celebrating for more than ten years with an ambitious initial aim, i.e. "by the year 2050, develop a team of fully autonomous humanoid robots that can win against the human world soccer champion team''. The current progress of individual learning for playing football has gained some positive results but is still far away from a human player. Soccer robots are still clumsy and inefficient. Improvements on the efficiency of individual robots, hence, need to be learned widely. This is also the aim of this project.

Biometrics systems for individual identification have been developed for decades. Many methods have been proposed such as fingerprint, facial, iris, voice recognitions. However each method has its own disadvantages. Fingerprint identifying systems usually have low security. Fingerprint patterns are easy to be counterfeited because they are left everywhere whenever we touch a surface. Similarly facial and voice patterns can also be cloned easily. Iris scanning is uncomfortable because of producing a strong light to shine into subjects’ eyes. A relatively new method that has been recommended by Hitachi Ltd can solve above problems and then can be applied in a wide area. It is to use finger veins for identifying individuals.

Biometrics systems for individual identification have been developed for decades. Many methods have been proposed such as fingerprint, facial, iris, voice recognitions. However each method has its own disadvantages. Fingerprint identifying systems usually have low security. Fingerprint patterns are easy to be counterfeited because they are left everywhere whenever we touch a surface. Similarly facial and voice patterns can also be cloned easily. Iris scanning is uncomfortable because of producing a strong light to shine into subjects’ eyes. A relatively new method that has been recommended by Hitachi Ltd can solve above problems and then can be applied in a wide area. It is to use finger veins for identifying individuals.

Vein images are taken by shining a near-infrared light through fingers. Hence finger vein scanning procedures do not require physical contact. Consequently it can be used in hospitals to identify patients with its hygiene. In addition, vein patterns are very hard to be cloned because the veins are inside our fingers. Finger vein recognition systems can obtain very high security and accuracy if the algorithm in use is good enough. Therefore they are suitable to be integrated in ATMs (automatic teller machines) and other applications which require high security.

Obviously finger vein security systems can be applied in many applications. As an undergraduate student project, this is limited in a small scale which enables personal computer authentication. The product has been tested with a small group of people and worked properly.  It still requires a deep test before commercialised in practice.

For the requisite of robotic implementations in many industries and daily human lives, numerous research has been
done widely by world class researchers. Although many implementations have been applied in different fields, robots
are still not clever enough to do complicated commitments. That is the reason RoboCup has been introduced in 1993
as a chance for researchers to investigate more about robotics tasks in a sophisticated game - football.

RoboCup is an annual competition for researchers from all over the world to do research about robot football which
requires most complicated individual skills and multi-agent cooperation. Although a large amount of research about
individual skills such as dribbling, passing, tackling skills has been done, further improvements are always essential
for the robots to play a better football.

In this thesis, three problems in RoboCup 2D simulation league are investigated. The first is the path planning
problem where the agent should find a path to a target position using 4 actions, i.e. dashing forward, dashing backward,
dashing to the left and dashing to the right. The second is to improve the dribbling skill in order for the agent to
move to a better position while keeping the ball still and intercepted by the opponents. The third is to help the
goalie better positioning in some emergent situations where an opponent is trying to shoot the ball. All experiments
were undertaken within the RoboCup 2D simulation version 13.2.0. The results have shown successful and partially
successful approaches which mean considerable contribution in the field.

Handwritten characters differ from person to person. Thus, when using traditional methods like Neural Networks and Image Processing techniques, extensive training of the system is needed. Due to this reason, an attempt was made to develop a system that used the methods that humans use to perceive handwritten characters. Thus, a system that recognizes features in handwritten characters using Fuzzy Logic was developed. The reasons for the selection of Fuzzy Logic were as follows: 1. Fuzzy Logic can be used to model human perception 2. The mathematics that Fuzzy Logic requires is extremely fundamental. Thus, any algorithm developed using fuzzy logic would require relatively simple and short calculations. 3. Due to simplicity of fuzzy calculations, they can be implemented on any hardware or software platform without too much concern for computing power. The objectives in the development of this method were: 1. The development of a short and efficient algorithm that tries as much as possible to model human perception. 2. The development of an algorithm that can be implemented on any hardware or software platform through low computational power requirements.

Handwritten characters differ from person to person. Thus, when using traditional methods like Neural Networks and Image Processing techniques, extensive training of the system is needed. Due to this reason, an attempt was made to develop a system that used the methods that humans use to perceive handwritten characters. Thus, a system that recognizes features in handwritten characters using Fuzzy Logic was developed. The reasons for the selection of Fuzzy Logic were as follows: 1. Fuzzy Logic can be used to model human perception 2. The mathematics that Fuzzy Logic requires is extremely fundamental. Thus, any algorithm developed using fuzzy logic would require relatively simple and short calculations. 3. Due to simplicity of fuzzy calculations, they can be implemented on any hardware or software platform without too much concern for computing power. The objectives in the development of this method were: 1. The development of a short and efficient algorithm that tries as much as possible to model human perception. 2. The development of an algorithm that can be implemented on any hardware or software platform through low computational power requirements. A time ordered sequence of coordinates should be sent into the system. The following calculations are then preformed on this set of data: 1. Segmentation of the image based on the direction of movement 2. Determination of the universe of discourse 3. Calculation of the local features for each identified segment 4. Application to Fuzzy Rule-Base for character recognition.

The main challenge in handwritten character recognition involves the development of a method that can generate descriptions of the handwritten objects in a short period of time. Due to its low computational requirement, fuzzy logic is probably the most efficient method available for on-line character recognition. The most tedious task associated with using fuzzy logic for online character recognition is the building of the rule-base that would describe the characters to be recognized. The problem is complicated as different people write the same character in complete different ways. This paper describes a method that can be used to generate a fuzzy value database that describes the characters written by different individuals.

The computational identification of genes in DNA sequences has become an issue of crucial importance due to the large number of DNA molecules being currently sequenced. We present a novel neural network based multi-classifier system, MultiNNProm, for the identification of promoter regions in E.Coli 1 DNA sequences. The DNA sequences were encoded using four different encoding methods and were used to train four different neural networks. The classification results of these neural networks were then aggregated using a variation of the LOP method. The aggregating weights used within the modified LOP aggregating algorithm were obtained through a genetic algorithm. We show that the use of different neural networks, trained on the same set of data, could provide slightly varying results if the data were differently encoded. We also show that the combination of more neural classifiers provides us with better accuracy than the individual networks.