ly, computational workflows may be understood as a directed acyclic graph (DAG), a kind of finite graph which contains no cycles and which must be traversed in a specific direction. In this representation, each node is an individual executable command. The edges in the DAG represent execution variables (data elements such as files or parameters) which pass from upstream nodes to downstream ones...

SUMMARY Given the positions of protein-coding genes along genomic sequence and probability values for protein alignments between genes, DAGchainer identifies chains of gene pairs sharing conserved order between genomic regions, by identifying paths through a directed acyclic graph (DAG). These chains of collinear gene pairs can represent segmentally duplicated regions and genes within a single ...

We consider a problem of elastic matching of time series. We propose an algorithm that automatically determines a subsequence b′ of a target time series b that best matches a query series a. In the proposed algorithm we map the problem of the best matching subsequence to the problem of a cheapest path in a DAG (directed acyclic graph). Our experimental results demonstrate that the proposed algo...

We present in this paper a constrained version of the problem of enumerating all maximal directed acyclic subgraphs (DAG) of a graph G. In this version, we enumerate stories, which are maximal DAGs whose sources and targets belong to a predefined subset of the nodes. First we show how to compute one story in polynomial-time, and we then describe two different algorithms to “tell” all possible s...

In this paper an optimal boundary encoding algorithm in the rate-distortion sense is proposed. Second-order Bspline curves are used to model object boundaries. An additive area distortion measure between the original boundary and its approximation is employed in the optimazation process. The problem is formulated in a Directed Acyclic Graph (DAG) paradigm, and the shortest path solution is used...

This paper examines the problem of efficient Bayesian computation in the context of linear Gaussian Directed Acyclic Graph (DAG) models. Unobserved latent variables are grouped together in a block, and sparse matrix techniques for computation are explored. Conditional sampling and likelihood computations are shown to be straightforward using a sparse matrix approach, allowing MCMC algorithms wi...

This is ICT’s first year of participation in the TDT evaluation. We participate in two tasks: Hierarchical Topic Detection (HTD) and Tracking. The two systems are both based on vector-space model. We use the method of multi-layered clustering to produce directed acyclic graph (DAG) of topics and improve the performance using the technology of traditional detection task. We only implement a base...

Uintah is a computational framework for fluid-structure interaction problems using a combination of adaptive mesh refinement(AMR) and MPM particle methods. Uintah uses domain decomposition and a task graph based approach for asynchronous communication and automatic message combination . The original task scheduler for Uintah ran computational tasks in a predefined order. To improve the performa...

This work considers the problem of learning linear Bayesian networks when some of the variables are unobserved. Identifiability and efficient recovery from low-order observable moments are established under a novel graphical constraint. The constraint concerns the expansion properties of the underlying directed acyclic graph (DAG) between observed and unobserved variables in the network, and it...

This paper concerns the assessment of direct causal effects from a combination of: (i) non­ experimental data, and (ii) qualitative do­ main knowledge. Domain knowledge is en­ coded in the form of a directed acyclic graph (DAG), in which all interactions are assumed linear, and some variables are presumed to be unobserved. We provide a generalization of the well-known method of Instrumental Var...