Numerical Analysis and Scientific Computing Commons^™

On The Design Of Advanced Filters For Biological Networks Using Graph Theoretic Properties, Kathryn Dempsey Cooper, Tzu-Yi Chen, Sanjukta Bhowmick, Hesham Ali

Interdisciplinary Informatics Faculty Proceedings & Presentations

Network modeling of biological systems is a powerful tool for analysis of high-throughput datasets by computational systems biologists. Integration of networks to form a heterogeneous model requires that each network be as noise-free as possible while still containing relevant biological information. In earlier work, we have shown that the graph theoretic properties of gene correlation networks can be used to highlight and maintain important structures such as high degree nodes, clusters, and critical links between sparse network branches while reducing noise. In this paper, we propose the design of advanced network filters using structurally related graph theoretic properties. While spanning …

The Development Of Parallel Adaptive Sampling Algorithms For Analyzing Biological Networks, Kathryn Dempsey Cooper, Kanimathi Duraisamy, Sanjukta Bhowmick, Hesham Ali

Interdisciplinary Informatics Faculty Proceedings & Presentations

The availability of biological data in massive scales continues to represent unlimited opportunities as well as great challenges in bioinformatics research. Developing innovative data mining techniques and efficient parallel computational methods to implement them will be crucial in extracting useful knowledge from this raw unprocessed data, such as in discovering significant cellular subsystems from gene correlation networks. In this paper, we present a scalable combinatorial sampling technique, based on identifying maximum chordal subgraphs, that reduces noise from biological correlation networks, thereby making it possible to find biologically relevant clusters from the filtered network. We show how selecting the appropriate filter …

A Novel Multithreaded Algorithm For Extracting Maximal Chordal Subgraphs, Mahantesh Halappanavar, John Feo, Kathryn Dempsey Cooper, Hesham Ali, Sanjukta Bhowmick

Interdisciplinary Informatics Faculty Proceedings & Presentations

Chordal graphs are triangulated graphs where any cycle larger than three is bisected by a chord. Many combinatorial optimization problems such as computing the size of the maximum clique and the chromatic number are NP-hard on general graphs but have polynomial time solutions on chordal graphs. In this paper, we present a novel multithreaded algorithm to extract a maximal chordal sub graph from a general graph. We develop an iterative approach where each thread can asynchronously update a subset of edges that are dynamically assigned to it per iteration and implement our algorithm on two different multithreaded architectures - Cray …

Comparing Methods For Interpolation To Improve Raster Digital Elevation Models, J. C. Guarneri, R. C. Weih Jr.

Journal of the Arkansas Academy of Science

Digital elevation models (DEMs) are available as raster files at 100m, 30m, and 10m resolutions for the contiguous United States and are used in a variety of geographic analyses. Some projects may require a finer resolution. GIS software offers many options for interpolating data to higher resolutions. We compared ten interpolation methods using 10m sample data from the Ouachita Mountains in central Arkansas. We interpolated the 10m DEM to 5m, 2.5m, and 1m resolutions and compared the absolute mean difference (AMD) for each using surveyed control points. Overall, there was little difference in the accuracy between interpolation methods at the …

Real-Time Anomaly Detection In Full Motion Video, Glenn Konowicz,, Jiang Li, Donnie Self (Ed.)

Electrical & Computer Engineering Faculty Publications

Improvement in sensor technology such as charge-coupled devices (CCD) as well as constant incremental improvements in storage space has enabled the recording and storage of video more prevalent and lower cost than ever before. However, the improvements in the ability to capture and store a wide array of video have required additional manpower to translate these raw data sources into useful information. We propose an algorithm for automatically detecting anomalous movement patterns within full motion video thus reducing the amount of human intervention required to make use of these new data sources. The proposed algorithm tracks all of the objects …

Structural Analysis In Multi-Relational Social Networks, Bingtian Dai, Freddy Chua, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Modern social networks often consist of multiple relations among individuals. Understanding the structure of such multi-relational network is essential. In sociology, one way of structural analysis is to identify different positions and roles using blockmodels. In this paper, we generalize stochastic blockmodels to Generalized Stochastic Blockmodels (GSBM) for performing positional and role analysis on multi-relational networks. Our GSBM generalizes many different kinds of Multivariate Probability Distribution Function (MVPDF) to model different kinds of multirelational networks. In particular, we propose to use multivariate Poisson distribution for multi-relational social networks.

Mining Diversity On Social Media Networks, Lu Liu, Feida Zhu, Meng Jiang, Jiawei Han, Lifeng Sun, Shiqiang Yang

Research Collection School Of Computing and Information Systems

The fast development of multimedia technology and increasing availability of network bandwidth has given rise to an abundance of network data as a result of all the ever-booming social media and social websites in recent years, e.g., Flickr, Youtube, MySpace, Facebook, etc. Social network analysis has therefore become a critical problem attracting enthusiasm from both academia and industry. However, an important measure that captures a participant’s diversity in the network has been largely neglected in previous studies. Namely, diversity characterizes how diverse a given node connects with its peers. In this paper, we give a comprehensive study of this concept. …

Who Is Retweeting The Tweeters? Modeling, Originating, And Promoting Behaviors In The Twitter Network, Achananuparp Palakorn, Ee Peng Lim, Jing Jiang, Tuan Anh Hoang

Research Collection School Of Computing and Information Systems

Real-time microblogging systems such as Twitter offer users an easy and lightweight means to exchange information. Instead of writing formal and lengthy messages, microbloggers prefer to frequently broadcast several short messages to be read by other users. Only when messages are interesting, are they propagated further by the readers. In this article, we examine user behavior relevant to information propagation through microblogging. We specifically use retweeting activities among Twitter users to define and model originating and promoting behavior. We propose a basic model for measuring the two behaviors, a mutual dependency model, which considers the mutual relationships between the two …

Improving The Knowledge-Based Expert System Lifecycle, Lucien Millette

UNF Graduate Theses and Dissertations

Knowledge-based expert systems are used to enhance and automate manual processes through the use of a knowledge base and modern computing power. The traditional methodology for creating knowledge-based expert systems has many commonly encountered issues that can prevent successful implementations. Complications during the knowledge acquisition phase can prevent a knowledge-based expert system from functioning properly. Furthermore, the time and resources required to maintain a knowledge-based expert system once implemented can become problematic. There are several concepts that can be integrated into a proposed methodology to improve the knowledge-based expert system lifecycle to create a more efficient process. These methods are …

The R Journal (December 2011) 3(2): Complete Issue, The R Foundation

The R Journal

Contributed Research Articles

Creating and Deploying an Application with (R)Excel and R, Thomas Baier, Erich Neuwirth, and Michele De Meo

glm2: Fitting Generalized Linear Models with Convergence Problems, Ian C. Marschner

Implementing the Compendium Concept with Sweave and DOCSTRIP, Michael Lundholm

Watch Your Spelling! Kurt Hornik and Duncan Murdoch

Ckmeans.1d.dp: Optimal k-means Clustering in One Dimension by Dynamic Programming, Haizhou Wang and Mingzhou Song

Nonparametric Goodness-of-Fit Tests for Discrete Null Distributions, Taylor B. Arnold and John W. Emerson

Using the Google Visualisation API with R, Markus Gesmann and Diego de Castillo

GrapheR: a Multiplatform GUI for Drawing Customizable Graphs in …

Rainbow: An R Package For Visualizing Functional Time Series, Han Lin Shang

The R Journal

Recent advances in computer technology have tremendously increased the use of functional data, whose graphical representation can be infinite-dimensional curves, images or shapes. This article describes four methods for visualizing functional time series using an R add-on package. These methods are demonstrated using age-specific Australian fertility data from 1921 to 2006 and monthly sea surface temperatures from January 1950 to December 2006.

Using The Google Visualisation Api With R, Markus Gesmann, Diego De Castillo

The R Journal

The googleVis package provides an interface between R and the Google Visualisation API to create interactive charts which can be embedded into web pages. The best known of these charts is probably the Motion Chart, popularised by Hans Rosling in his TED talks. With the googleVis package users can easily create web pages with interactive charts based on R data frames and display them either via the local R HTTPhelp server or within their own sites.

Ckmeans.1d.Dp: Optimal K-Means Clustering In One Dimension By Dynamic Programming, Haizhou Wang, Mingzhou Song

The R Journal

The heuristic k-means algorithm, widely used for cluster analysis, does not guarantee optimality. We developed a dynamic programming algorithm for optimal one-dimensional clustering. The algorithm is implemented as an R package called Ckmeans.1d.dp. We demonstrate its ad vantage in optimality and runtime over the standard iterative k-means algorithm.

Implementing The Compendium Concept With Sweave And Docstrip, Michael Lundholm

The R Journal

This article suggests an implementation of the compendium concept by combining Sweave and the LATEX literate programming environment DOCSTRIP.

Content-Based Social Network Analysis Of Mailing Lists, Angela Bohn, Ingo Feinerer, Kurt Hornik, Patrick Mair

The R Journal

Social Network Analysis (SNA) provides tools to examine relationships between people. Text Mining (TM) allows capturing the text they produce in Web 2.0 applications, for example, however it neglects their social structure. This paper applies an approach to combine the two methods named “content-based SNA”. Using the R mailing lists, R-help and R-devel, we show how this combination can be used to describe people’s interests and to find out if authors who have similar interests actually communicate. We find that the expected positive relationship between sharing interests and communicating gets stronger as the centrality scores of authors in the communication …

Watch Your Spelling!, Kurt Hornik, Duncan Murdoch

The R Journal

We discuss the facilities in base R for spell checking via Aspell, Hunspell or Ispell, which are useful in particular for conveniently checking the spelling of natural language texts in package Rd files and vignettes. Spell checking performance is illustrated using the Rd files in package stats. This example clearly indicates the need for a domain-specific statistical dictionary. We analyze the results of spell checking all Rd files in all CRAN packages and show how these can be employed for building such a dictionary.

Nonparametric Goodness-Of-Fit Tests For Discrete Null Distributions, Taylor B. Arnold, John W. Emerson

The R Journal

Methodology extending nonparametric goodness-of-fit tests to discrete null distributions has existed for several decades. However, modern statistical software has generally failed to provide this methodology to users. We offer a revision of R’s ks.test() function and a new cvm.test() function that fill this need in the R language for two of the most popular nonparametric goodness-of-fit tests. This paper describes these contributions and provides examples of their usage. Particular attention is given to various numerical issues that arise in their implementation.

Portable C++ For R Packages, Martyn Plummer

The R Journal

Package checking errors are more common on Solaris than Linux. In many cases, these errors are due to non-portable C++ code. This article reviews some commonly recurring problems in C++ code found in R packages and suggests solutions.

Glm2: Fitting Generalized Linear Models With Convergence Problems, Ian C. Marschner

The R Journal

The R function glm uses step-halving to deal with certain types of convergence problems when using iteratively reweighted least squares to fit a generalized linear model. This works well in some circumstances but non-convergence remains a possibility, particularly with a non standard link function. In some cases this is be cause step-halving is never invoked, despite a lack of convergence. In other cases step-halving is invoked but is unable to induce convergence. One remedy is to impose a stricter form of step halving than is currently available in glm, so that the deviance is forced to decrease in every iteration. …

Creating And Deploying An Application With (R)Excel And R, Thomas Baier, Erich Neuwirth, Michele De Meo

The R Journal

We present some ways of using R in Excel and build an example application using the package rpart. Starting with simple interactive use of rpart in Excel, we eventually package the code into an Excel-based application, hiding all details (including R itself) from the end user. In the end, our application implements a service oriented architecture (SOA) with a clean separation of presentation and computation layer