Bayesian networks are probabilistic graphical models widely employed to understand dependencies in high-dimensional data, and even facilitate causal discovery. Learning the underlying network structure, which is encoded as a directed acyclic graph (DAG) highly challenging mainly due vast number of possible combination with acyclicity constraint. Efforts have focused on two fronts: constraint-ba...

We establish a new framework for statistical estimation of directed acyclic graphs (DAGs) when data are generated from a linear, possibly non-Gaussian structural equation model. Our framework consists of two parts: (1) inferring the moralized graph from the support of the inverse covariance matrix; and (2) selecting the best-scoring graph amongst DAGs that are consistent with the moralized grap...

This research paper presents an indepth examination of the security aspects Multiple Zone Direct Acyclic Graph Blockchain (MZ-DAG Blockchain) framework. The MZ-DAG features a layer that implements non-clonable physical function-based validation mechanism for authentication multiple certificates within blockchain. To enhance this layer, study proposes integration lightweight intrusion detection ...

In this additional file, the directed acyclic graph (DAG) of the model and the flowchart of the proposed uBLU algorithm are provided. More results on synthetic datasets are presented to validate the proposed Bayesian algorithm. 1 Summary of the model We consider the model described in the paper (see Section " Methods ") We propose to project the factors m r (r = 1,. .. , R) into a lower subspac...

We discuss directed acyclic graph (DAG) models to represent the independence structure of linear Gaussian systems with continuous variables: such models can be interpreted as a set of recursive univariate regressions. Then we consider Gaussian models in which one of the variables is not observed and we show how the incomplete log-likelihood of the observed data can be maximized using the EM. As...

A grid computing environment involves cooperation and sharing resources among distributed machines. Users may dispatch their tasks to remote computing resources instead of just computing locally. Hence, task assignment is an important issue in a grid computing environment. It plays a crucial role in achieving high utilisation of resources. Due to heterogeneity of resources, assigning an applica...

Optical network, which can provide guaranteed quality of service (QoS) connections, is considered as a promising infrastructure for grid computing to solve more and more complex scientific problems. When optical links are regarded as resources and jointly scheduled with other grid resources, communication contention must be taken into consideration for efficient task scheduling. This paper mode...

Given a digraph D, the Minimum Leaf Out-Branching problem (MinLOB) is the problem of finding in D an out-branching with the minimum possible number of leaves, i.e., vertices of out-degree 0. Gutin, Razgon and Kim (2008) proved that MinLOB is polynomial time solvable for acyclic digraphs which are exactly the digraphs of directed path-width (DAG-width, directed tree-width, respectively) 0. We in...

Recently, there has been renewed interest in semantic dependency parsing, among which one of the paradigms focuses on parsing directed acyclic graphs (DAGs). Consideration of the decoding problem in natural language semantic dependency parsing as finding a maximum spanning DAG of a weighted directed graph carries many complexities. In particular, the computational complexity (and approximabilit...

Provenance research has generally focused on issues with data collection and organization. Most approaches represent stored provenance data as a directed acyclic graph (DAG), where objects such as files and processes are nodes in the graph and directed edges specify ancestry relationships between them. While there has been some work addressing logical compression of these provenance graphs, eff...