A Parallel Algorithm for Exact Bayesian Structure Discovery in Bayesian Networks

Exact Bayesian structure discovery in Bayesian networks requires exponential time and space. Using dynamic programming (DP), the fastest known sequential algorithm computes the exact posterior probabilities of structural features in $O(2(d+1)n2^n)$ time and space, if the number of nodes (variables) in the Bayesian network is $n$ and the in-degree (the number of parents) per node is bounded by a constant $d$. Here we present a parallel algorithm capable of computing the exact posterior probabilities for all $n(n-1)$ edges with optimal parallel space efficiency and nearly optimal parallel time efficiency. That is, if $p=2^k$ processors are used, the run-time reduces to $O(5(d+1)n2^{n-k}+k(n-k)^d)$ and the space usage becomes $O(n2^{n-k})$ per processor. Our algorithm is based the observation that the subproblems in the sequential DP algorithm constitute a $n$-$D$ hypercube. We take a delicate way to coordinate the computation of correlated DP procedures such that large amount of data exchange is suppressed. Further, we develop parallel techniques for two variants of the well-known \emph{zeta transform}, which have applications outside the context of Bayesian networks. We demonstrate the capability of our algorithm on datasets with up to 33 variables and its scalability on up to 2048 processors. We apply our algorithm to a biological data set for discovering the yeast pheromone response pathways.Comment: 32 pages, 12 figure

Finite Volume Simulation Framework for Die Casting with Uncertainty Quantification

The present paper describes the development of a novel and comprehensive computational framework to simulate solidification problems in materials processing, specifically casting processes. Heat transfer, solidification and fluid flow due to natural convection are modeled. Empirical relations are used to estimate the microstructure parameters and mechanical properties. The fractional step algorithm is modified to deal with the numerical aspects of solidification by suitably altering the coefficients in the discretized equation to simulate selectively only in the liquid and mushy zones. This brings significant computational speed up as the simulation proceeds. Complex domains are represented by unstructured hexahedral elements. The algebraic multigrid method, blended with a Krylov subspace solver is used to accelerate convergence. State of the art uncertainty quantification technique is included in the framework to incorporate the effects of stochastic variations in the input parameters. Rigorous validation is presented using published experimental results of a solidification problem

Uncertainty Quantification in Three Dimensional Natural Convection using Polynomial Chaos Expansion and Deep Neural Networks

This paper analyzes the effects of input uncertainties on the outputs of a three dimensional natural convection problem in a differentially heated cubical enclosure. Two different cases are considered for parameter uncertainty propagation and global sensitivity analysis. In case A, stochastic variation is introduced in the two non-dimensional parameters (Rayleigh and Prandtl numbers) with an assumption that the boundary temperature is uniform. Being a two dimensional stochastic problem, the polynomial chaos expansion (PCE) method is used as a surrogate model. Case B deals with non-uniform stochasticity in the boundary temperature. Instead of the traditional Gaussian process model with the Karhunen-Lo$\grave{e}$ve expansion, a novel approach is successfully implemented to model uncertainty in the boundary condition. The boundary is divided into multiple domains and the temperature imposed on each domain is assumed to be an independent and identically distributed (i.i.d) random variable. Deep neural networks are trained with the boundary temperatures as inputs and Nusselt number, internal temperature or velocities as outputs. The number of domains which is essentially the stochastic dimension is 4, 8, 16 or 32. Rigorous training and testing process shows that the neural network is able to approximate the outputs to a reasonable accuracy. For a high stochastic dimension such as 32, it is computationally expensive to fit the PCE. This paper demonstrates a novel way of using the deep neural network as a surrogate modeling method for uncertainty quantification with the number of simulations much fewer than that required for fitting the PCE, thus, saving the computational cost

An Adaptive Parallel Algorithm for Computing Connected Components

We present an efficient distributed memory parallel algorithm for computing connected components in undirected graphs based on Shiloach-Vishkin's PRAM approach. We discuss multiple optimization techniques that reduce communication volume as well as load-balance the algorithm. We also note that the efficiency of the parallel graph connectivity algorithm depends on the underlying graph topology. Particularly for short diameter graph components, we observe that parallel Breadth First Search (BFS) method offers better performance. However, running parallel BFS is not efficient for computing large diameter components or large number of small components. To address this challenge, we employ a heuristic that allows the algorithm to quickly predict the type of the network by computing the degree distribution and follow the optimal hybrid route. Using large graphs with diverse topologies from domains including metagenomics, web crawl, social graph and road networks, we show that our hybrid implementation is efficient and scalable for each of the graph types. Our approach achieves a runtime of 215 seconds using 32K cores of Cray XC30 for a metagenomic graph with over 50 billion edges. When compared against the previous state-of-the-art method, we see performance improvements up to 24x

Atomic-scale coexistence of short-range magnetic order and superconductivity in Fe1+y_{1+y}Se0.1_{0.1}Te0.9_{0.9}

The ground state of the parent compounds of many high temperature superconductors is an antiferromagnetically (AFM) ordered phase, where superconductivity emerges when the AFM phase transition is suppressed by doping or application of pressure. This behaviour implies a close relation between the two orders. Understanding the interplay between them promises a better understanding of how the superconducting condensate forms from the AFM ordered background. Here we explore this relation in real space at the atomic scale using low temperature spin-polarized scanning tunneling microscopy (SP-STM) and spectroscopy. We investigate the transition from antiferromagnetically ordered $\mathrm{Fe}_{1+y}\mathrm{Te}$ via the spin glass phase in $\mathrm{Fe}_{1+y}\mathrm{Se}_{0.1}\mathrm{Te}_{0.9}$ to superconducting $\mathrm{Fe}_{1+y}\mathrm{Se}_{0.15}\mathrm{Te}_{0.85}$. In $\mathrm{Fe}_{1+y}\mathrm{Se}_{0.1}\mathrm{Te}_{0.9}$ we observe an atomic-scale coexistence of superconductivity and short-ranged bicollinear antiferromagnetic order.Comment: 7 pages, 6 figure

Squarepants in a Tree: Sum of Subtree Clustering and Hyperbolic Pants Decomposition

We provide efficient constant factor approximation algorithms for the problems of finding a hierarchical clustering of a point set in any metric space, minimizing the sum of minimimum spanning tree lengths within each cluster, and in the hyperbolic or Euclidean planes, minimizing the sum of cluster perimeters. Our algorithms for the hyperbolic and Euclidean planes can also be used to provide a pants decomposition, that is, a set of disjoint simple closed curves partitioning the plane minus the input points into subsets with exactly three boundary components, with approximately minimum total length. In the Euclidean case, these curves are squares; in the hyperbolic case, they combine our Euclidean square pants decomposition with our tree clustering method for general metric spaces.Comment: 22 pages, 14 figures. This version replaces the proof of what is now Lemma 5.2, as the previous proof was erroneou

Early life exposure to low levels of AHR agonist PCB126 (3,3’,4,4’,5- pentachlorobiphenyl) reprograms gene expression in adult brain

Author Posting. © The Author(s), 2017. This is the author's version of the work. It is posted here under a nonexclusive, irrevocable, paid-up, worldwide license granted to WHOI. It is made available for personal use, not for redistribution. The definitive version was published in Toxicological Sciences 160 (2017): 386-397, doi:10.1093/toxsci/kfx192.Early life exposure to environmental chemicals can have long-term consequences that are not always apparent until later in life. We recently demonstrated that developmental exposure of zebrafish to low, non-embryotoxic levels of 3,3’,4,4’,5-pentachlorobiphenyl (PCB126) did not affect larval behavior, but caused changes in adult behavior. The objective of this study was to investigate the underlying molecular basis for adult behavioral phenotypes resulting from early life exposure to PCB126. We exposed zebrafish embryos to PCB126 during early development and measured transcriptional profiles in whole embryos, larvae and adult male brains using RNA-sequencing. Early life exposure to 0.3 nM PCB126 induced cyp1a transcript levels in 2-dpf embryos, but not in 5-dpf larvae, suggesting transient activation of aryl hydrocarbon receptor with this treatment. No significant induction of cyp1a was observed in the brains of adults exposed as embryos to PCB126. However, a total of 2209 and 1628 genes were differentially expressed in 0.3 nM and 1.2 nM PCB126-exposed groups, respectively. KEGG pathway analyses of upregulated genes in the brain suggest enrichment of calcium signaling, MAPK and notch signaling, and lysine degradation pathways. Calcium is an important signaling molecule in the brain and altered calcium homeostasis could affect neurobehavior. The downregulated genes in the brain were enriched with oxidative phosphorylation and various metabolic pathways, suggesting that the metabolic capacity of the brain is impaired. Overall, our results suggest that PCB exposure during sensitive periods of early development alters normal development of the brain by reprogramming gene expression patterns, which may result in alterations in adult behavior