Learning Large-Scale Bayesian Networks with the sparsebn Package

Learning graphical models from data is an important problem with wide applications, ranging from genomics to the social sciences. Nowadays datasets often have upwards of thousands---sometimes tens or hundreds of thousands---of variables and far fewer samples. To meet this challenge, we have developed a new R package called sparsebn for learning the structure of large, sparse graphical models with a focus on Bayesian networks. While there are many existing software packages for this task, this package focuses on the unique setting of learning large networks from high-dimensional data, possibly with interventions. As such, the methods provided place a premium on scalability and consistency in a high-dimensional setting. Furthermore, in the presence of interventions, the methods implemented here achieve the goal of learning a causal network from data. Additionally, the sparsebn package is fully compatible with existing software packages for network analysis.Comment: To appear in the Journal of Statistical Software, 39 pages, 7 figure

A unified data flow model for fault tolerant computers

The Dataflow Simulation System (DFSS) at USL was used as the medium on which a functional simulaton of sIFT was produced. DFSS is written in PL/I and is supported by MULTICS. Within the simulation, all the interprocessor communication, fault simulation, system state data, and monitoring were implemented in dataflow and supported directly by DFSS. The actual processor level computation was carried out by the SIFT code in PASCAL. The interface between DFSS in PL/I and the SIFT code in PASCAL was supported under a mechanism in DFSS called a Node Realization Module (NRM)

A model-derivation framework for timing analysis of Java software Systems

One of the main challenges in developing a software system is to assure that its properties fulfill the specifications. In the context of this paper, we are especially interested in timing properties. Model-based software verification is one of the approaches to achieve this. However, model-based verification requires expressive models of software systems and deriving such models is not a trivial task. Although there are a few model derivation tool proposals for the purpose of model-checking timing properties, these are dedicated tools supporting a selected set of verification techniques and as such they are not explicitly designed for coping with new demands. This paper presents a framework that derives models from Java programs in an automated way for analyzing timing properties. The framework has the following properties that are not provided by the previous proposals: (1) Efficiency in model development, (2) consistency of models with software, (3) expressiveness of models, (4) scalability and (5) extensibility of the model derivation process

Doctor of Philosophy

dissertationThe design of integrated circuit (IC) requires an exhaustive verification and a thorough test mechanism to ensure the functionality and robustness of the circuit. This dissertation employs the theory of relative timing that has the advantage of enabling designers to create designs that have significant power and performance over traditional clocked designs. Research has been carried out to enable the relative timing approach to be supported by commercial electronic design automation (EDA) tools. This allows asynchronous and sequential designs to be designed using commercial cad tools. However, two very significant holes in the flow exist: the lack of support for timing verification and manufacturing test. Relative timing (RT) utilizes circuit delay to enforce and measure event sequencing on circuit design. Asynchronous circuits can optimize power-performance product by adjusting the circuit timing. A thorough analysis on the timing characteristic of each and every timing path is required to ensure the robustness and correctness of RT designs. All timing paths have to conform to the circuit timing constraints. This dissertation addresses back-end design robustness by validating full cyclical path timing verification with static timing analysis and implementing design for testability (DFT). Circuit reliability and correctness are necessary aspects for the technology to become commercially ready. In this study, scan-chain, a commercial DFT implementation, is applied to burst-mode RT designs. In addition, a novel testing approach is developed along with scan-chain to over achieve 90% fault coverage on two fault models: stuck-at fault model and delay fault model. This work evaluates the cost of DFT and its coverage trade-off then determines the best implementation. Designs such as a 64-point fast Fourier transform (FFT) design, an I2C design, and a mixed-signal design are built to demonstrate power, area, performance advantages of the relative timing methodology and are used as a platform for developing the backend robustness. Results are verified by performing post-silicon timing validation and test. This work strengthens overall relative timed circuit flow, reliability, and testability

netgwas: An R Package for Network-Based Genome-Wide Association Studies

Graphical models are powerful tools for modeling and making statistical inferences regarding complex associations among variables in multivariate data. In this paper we introduce the R package netgwas, which is designed based on undirected graphical models to accomplish three important and interrelated goals in genetics: constructing linkage map, reconstructing linkage disequilibrium (LD) networks from multi-loci genotype data, and detecting high-dimensional genotype-phenotype networks. The netgwas package deals with species with any chromosome copy number in a unified way, unlike other software. It implements recent improvements in both linkage map construction (Behrouzi and Wit, 2018), and reconstructing conditional independence network for non-Gaussian continuous data, discrete data, and mixed discrete-and-continuous data (Behrouzi and Wit, 2017). Such datasets routinely occur in genetics and genomics such as genotype data, and genotype-phenotype data. We demonstrate the value of our package functionality by applying it to various multivariate example datasets taken from the literature. We show, in particular, that our package allows a more realistic analysis of data, as it adjusts for the effect of all other variables while performing pairwise associations. This feature controls for spurious associations between variables that can arise from classical multiple testing approach. This paper includes a brief overview of the statistical methods which have been implemented in the package. The main body of the paper explains how to use the package. The package uses a parallelization strategy on multi-core processors to speed-up computations for large datasets. In addition, it contains several functions for simulation and visualization. The netgwas package is freely available at https://cran.r-project.org/web/packages/netgwasComment: 32 pages, 9 figures; due to the limitation "The abstract field cannot be longer than 1,920 characters", the abstract appearing here is slightly shorter than that in the PDF fil

Extreme Scale De Novo Metagenome Assembly

Metagenome assembly is the process of transforming a set of short, overlapping, and potentially erroneous DNA segments from environmental samples into the accurate representation of the underlying microbiomes's genomes. State-of-the-art tools require big shared memory machines and cannot handle contemporary metagenome datasets that exceed Terabytes in size. In this paper, we introduce the MetaHipMer pipeline, a high-quality and high-performance metagenome assembler that employs an iterative de Bruijn graph approach. MetaHipMer leverages a specialized scaffolding algorithm that produces long scaffolds and accommodates the idiosyncrasies of metagenomes. MetaHipMer is end-to-end parallelized using the Unified Parallel C language and therefore can run seamlessly on shared and distributed-memory systems. Experimental results show that MetaHipMer matches or outperforms the state-of-the-art tools in terms of accuracy. Moreover, MetaHipMer scales efficiently to large concurrencies and is able to assemble previously intractable grand challenge metagenomes. We demonstrate the unprecedented capability of MetaHipMer by computing the first full assembly of the Twitchell Wetlands dataset, consisting of 7.5 billion reads - size 2.6 TBytes.Comment: Accepted to SC1