Causal learning methods are often evaluated in terms of their ability to discover a true underlying directed acyclic graph (DAG) structure. However, in general the true structure is unknown and may not be a DAG structure. We therefore consider evaluating causal learning methods in terms of predicting the effects of interventions on unseen test data. Given this task, we show that there exist a v...

Munc13-1 is a presynaptic protein with an essential role in synaptic vesicle priming. It contains a diacylglycerol (DAG)/beta phorbol ester binding C(1) domain and is a potential target of the DAG second messenger pathway that may act in parallel with PKCs. Using genetically modified mice that express a DAG/beta phorbol ester binding-deficient Munc13-1(H567K) variant instead of the wild-type pr...

In this paper, we present our method which is a performance improvement to the Directed Acyclic Graph Support Vector Machines (DAG SVM). It suggests a weighted multi-class classification technique which divides the input space into several subspaces. In the training phase of the technique, for each subspace, a DAG SVM is trained and its probability density function (pdf) is guesstimated. In the...

We study DAG-depth, a structural depth measure of directed graphs, which naturally extends the treedepth of ordinary graphs. We define a DAG-depth decomposition as a strategy for the cop player in the lift-free version of the cops-and-robber game on directed graphs and prove its correctness. The DAG-depth decomposition is related to DAG-depth in a similar way as an elimination tree is related t...

The diacylglycerol (DAG) signal generated from membrane phospholipids by hormone-activated phospholipases is attenuated by mechanisms that include lipolysis or phospholipid resynthesis. To determine whether the DAG signal might also be terminated by incorporation of DAG into triacylglycerol (TAG), we studied the direct formation of TAG from endogenous DAG generated by bacterial phospholipase C ...

a r t i c l e i n f o This paper deals with the estimation of state changes in system descriptions for dynamic Bayesian networks (DBNs) by using a genetic procedure and particle filters (PFs). We extend the DBN scheme to more general cases with unknown Directed Acyclic Graph (DAG) and state changes. First, we summarize the basic model of DBN where the DAG can be changed and the state transition...

We investigate parameter priors for discrete DAG models. It was shown in previous works that a Dirichlet prior on the parameters of a discrete DAG model is inevitable assuming global and local parameter independence for all possible complete DAG structures. A similar result for Gaussian DAG models hinted that the assumption of local independence may be redundant. Herein, we prove that the local...

Landmark localization plays an important role in medical image analysis. Learning based methods, including convolutional neural network (CNN) and graph (GCN), have demonstrated the state-of-the-art performance. However, most of these methods are fully-supervised heavily rely on manual labeling a large training dataset. In this paper, graph-based method, deep adaptive (DAG), we proposed semi-sup...

Several real problems ranging from text classification to computational biology are characterized by hierarchical multi-label classification tasks. Most of the methods presented in literature focused on tree-structured taxonomies, but only few on taxonomies structured according to a Directed Acyclic Graph (DAG). In this contribution novel classification ensemble algorithms for DAG-structured ta...

Consideration of the decoding problem in semantic parsing as finding a maximum spanning DAG of a weighted directed graph carries many complexities that haven’t been fully addressed in the literature to date, among which are its actual appropriateness for the decoding task in semantic parsing, not to mention an explicit proof of its complexity (and its approximability). In this paper, we conside...