40155382
Efficient Solution of Bidomain Equations in Simulation of Cardiac Excitation Anisotropic Propagation
Bidomain equations are used to characterize myocardial physiological activation and propagation. Their numerical solution is costly computation because of the higher temporal and spatial discretization requirements, especially in three dimensions. In most previous studies, the heart was supposed to be homogeneous isotropic medium, and thus can use the mondomain equation in stead of the bidomain equations to simulate the heart excitation propagation. Simulation of heart excitation anisotropic propagation in three-dimensional large tissue size by solving bidomain equations has not been implemented yet. In this paper, we present an efficient solution of bidomain equations in simulation of heart excitation anisotropic propagation by combining some numerical techniques such as non-standard finite difference (NSFD), domain decomposition and multigrid methods. The results show that the proposed method can successfully be used to simulate heart excitation anisotropic propagation in three-dimensional large tissue size, and it suggests that such method may provide a good basis for heart simulation research in a more physiologically way.
36668943
Parallel Solution in Simulation of Cardiac Excitation Anisotropic Propagation
Solution in simulation of cardiac excitation anisotropic propagation throughout the ventricular myocardium is computationally very expensive that demands the introduction of a high performance computing techniques. In this study, a canine ventricle model was constructed features a realistic anatomical structure, including intramural fiber rotation and a conduction system. By using operator-splitting scheme, adaptive time step and backward differentiation formulation techniques in a parallel implement, we solved mondomain equation successfully. The stimulation produced isochrone's map is close to the clinical record that obtained from the non-contact mapping system of Ensite 3000. The results show that the proposed methods can successfully be used to simulate heart excitation anisotropic propagation in three-dimensional anatomical large tissue size.
9190159
Dynamic Bayesian Network (DBN) with Structure Expectation Maximization (SEM) for Modeling of Gene Network from Time Series Gene Expression Data
Abstract Exploring gene regulatory network is a key topic in molecular biology. In this paper, we present a new dynamic Bayesian network (DBN) framework embedded with structural expectation maximization (SEM) to model gene relationship. It is well-suited for analyzing the time-series data and can deal with cyclical structures that can not be tackled by static Bayesian network. We applied the new method to learning the regulatory network and the metabolic pathway from Saccharomyces Cerevisiae cell cycle gene expression data. The results show that the proposed method is capable of handling missing values in expression data sets, and the inference accuracy can further be improved. Keyword: Microarrays; Gene regulatory networks; Dynamic Bayesian network; Structural expectation maximization 1. Introduction The establishment of gene regulatory network is critical to the understanding of the genetic regulation process. This problem has become an important challenge in recent years. The invention of microarray technology is viewed as a milestone, which helps scientist measure expression levels of thousands of genes simultaneously. Several methods have been presented so far to learn gene network from microarray data, such as Boolean networks [2, 3], differential equations [4, 5], and Bayesian networks [6-8]. Among all of them, Bayesian network based approach has received a lot of attention because of the probabilistic nature of this model. It can be used to learn causal relationship and particularly, combine it with prior knowledge readily. However, there exist a lot of shortcomings for the static version of Bayesian network. First, it is unable to capture the temporal information. Second, it is impossible to model cyclic network, which is often considered to be an accurate description of real gene regulation mechanism. In this paper, we employ dynamic Bayesian network (DBN) [1, 9,10], instead of its earlier static version, to model a gene network with cyclic regulation. In general, the DBN is well-suited for characterizing time-series gene expression data. Owing to the limitation of experimental condition, there are many missing values in the gene expression data sets, which usually have an impact on the inference accuracy. To address this problem, we propose a new DBN model embedded with
373663
A Multi-Level Constraint-Based Controller for the Dynamo98 Robot Soccer Team
Constraint Nets provide a semantic model for modeling hybrid dynamic systems. Controllers are embedded constraint solvers that solve constraints in real-time. A controller for our new softbot soccer team, UBC Dynamo98, has been modeled in Constraint Nets, and implemented in Java, using the Java Beans architecture. An evolutionary algorithm is designed and implemented to adjust the weights of constraints in the controller. The paper demonstrates that the formal Constraint Net approach is a practical tool for designing and implementing controllers for robots in multi-agent real-time environments.
21616351
Palmprint Recognition Using a Novel Sparse Coding Technique
This paper proposes a novel recognition method for palmprints using a new sparse coding (SC) algorithm proposed by us. This algorithm exploited the maximum Kurtosis as the sparse measure criterion, at one time, a fixed variance term of sparse coefficients is used to yield a fixed information capacity. Experimental results show that the feature basis vectors of palmprint images can be successfully extracted by using our SC algorithm. Using the radial basis probabilistic neural network (RBPNN), the classification task can be implemented easily. Finally, compared with methods of principal component analysis (PCA) and the classical SC, simulation results show that our algorithm is indeed efficient and effective in performing palmprint recognition task.
16356479
A multi-agent simulation for social agents
Agent-based modeling of human social behavior is an increasingly important research area. Many fully functional multi-agent systems have been developed and have been put to use over the years. But efficient scalable and robust social systems are difficult to engineer, both from the modeling perspective and the implementation perspective. There exist three important difficulties. First, the system needs to have an adaptable agent framework that can successfully make intuitive and deliberative decisions much like a real social participant would. Secondly, the system must have a robust architecture that not only ensures its functioning no matter the simulation, but also provides an easily understood interface that researchers can interact with while running their simulations. Finally, the system must be effectively distributed to handle the necessary number of agents that social research requires to obtain meaningful results. This paper presents our work on creating a multi-agent simulation for social agents that would overcome these three difficulties. Lastly, we present a simulation to test the system and provide the results of that simulation.
35383347
A Power Law for Connectedness of Some Random Graphs at the Critical Point
We consider P(G is connected) when G is a graph with vertex set Z+ = {1,2, …}, and the edge between i and j is present with probability p(i, j) = min(λ h(i, j), 1) for certain functions h(i, j) homogeneous of degree -1. It is known that there is a critical value λc of λ such that 
 
. 
 
We show that the probability, at the critical point λc, that n1, and n2 are connected satisfies a power law, in the sense that for n2 ≧ nt ≧ 1 
 
for any δ > 0 and certain constants c1 and c2.