Numerical Analysis and Scientific Computing Commons^™

Semicomprisks: An R Package For The Analysis Of Independent And Cluster-Correlated Semi-Competing Risks Data, Danilo Alvares, Sebastien Haneuse, Catherine Lee, Kyu Ha Lee

The R Journal

Semi-competing risks refer to the setting where primary scientific interest lies in estimation and inference with respect to a non-terminal event, the occurrence of which is subject to a terminal event. In this paper, we present the R package SemiCompRisks that provides functions to perform the analysis of independent/clustered semi-competing risks data under the illness-death multi-state model. The package allows the user to choose the specification for model components from a range of options giving users substantial flexibility, including: accelerated failure time or proportional hazards regression models; parametric or non-parametric specifications for baseline survival functions; parametric or non-parametric specifications for …

R News, R Core Team

The R Journal

CHANGES IN R 3.6.1

Fixed Point Acceleration In R, Stuart Baumann, Margaryta Klymak

The R Journal

t A fixed point problem is one where we seek a vector, X, for a function, f, such that f(X) = X. The solution of many such problems can be accelerated by using a fixed point acceleration algorithm. With the release of the FixedPoint package there is now a number of algorithms available in R that can be used for accelerating the finding of a fixed point of a function. These algorithms include Newton acceleration, Aitken acceleration and Anderson acceleration as well as epsilon extrapolation methods and minimal polynomial methods. This paper demonstrates the use of fixed point accelerators in …

Nowcasting: An R Package For Predicting Economic Variables Using Dynamic Factor Models, Serge De Valk, Daiane De Mattos, Pedro Ferreira

The R Journal

The nowcasting package provides the tools to make forecasts of monthly or quarterly economic variables using dynamic factor models. The objective is to help the user at each step of the forecasting process, starting with the construction of a database, all the way to the interpretation of the forecasts. The dynamic factor model adopted in this package is based on the articles from Giannone et al. (2008) and Banbura et al. (2011). Although there exist several other dynamic factor model packages available for R, ours provides an environment to easily forecast economic variables and interpret results.

Unival: An Fa-Based R Package For Assessing Essential Unidimensionality Using External Validity Information, Pere J. Ferrando, Urbano Lorenzo-Seva, David Navarro-Gonzalez

The R Journal

The unival package is designed to help researchers decide between unidimensional and correlated-factors solutions in the factor analysis of psychometric measures. The novelty of the approach is its use of external information, in which multiple factor scores and general factor scores are related to relevant external variables or criteria. The unival package’s implementation comes from a series of procedures put forward by Ferrando and Lorenzo-Seva (2019) and new methodological developments proposed in this article. We assess models fitted using unival by means of a simulation study extending the results obtained in the original proposal. Its usefulness is also assessed through …

Optimparallel: An R Package Providing A Parallel Version Of The L-Bfgs-B Optimization Method, Florian Gerber, Reinhard Furrer

The R Journal

The R package optimParallel provides a parallel version of the L-BFGS-B optimization method of optim(). The main function of the package is optimParallel(), which has the same usage and output as optim(). Using optimParallel() can significantly reduce the optimization time, especially when the evaluation time of the objective function is large and no analytical gradient is available. We introduce the R package and illustrate its implementation, which takes advantage of the lexical scoping mechanism of R.

Integration Of Networks And Pathways With Starbiotrek Package, Claudia Cava, Isabella Castiglioni

The R Journal

High-throughput genomic technologies bring to light a comprehensive hallmark of molecular changes of a disease. It is increasingly evident that genes are not isolated from each other and the identification of a gene signature can only partially elucidate the de-regulated biological functions in a disease. The comprehension of how groups of genes (pathways) are related to each other (pathway-cross talk) could explain biological mechanisms causing diseases. Biological pathways are important tools to identify gene interactions and decrease the large number of genes to be studied by partitioning them into smaller groups. Furthermore, recent scientific studies have demonstrated that an integration …

Whats For Dynr: A Package For Linear And Nonlinear Dynamic Modeling In R, Lu Ou, Michael D. Hunter, Sy-Miin Chow

The R Journal

Intensive longitudinal data in the behavioral sciences are often noisy, multivariate in nature, and may involve multiple units undergoing regime switches by showing discontinuities interspersed with continuous dynamics. Despite increasing interest in using linear and nonlinear differential/difference equation models with regime switches, there has been a scarcity of software packages that are fast and freely accessible. We have created an R package called dynr that can handle a broad class of linear and nonlinear discrete- and continuous-time models, with regime-switching properties and linear Gaussian measurement functions, in C, while maintaining simple and easy-to-learn model specification functions in R. We present …

Swgee: An R Package For Analyzing Longitudinal Data With Response Missingness And Covariate Measurement Error, Juan Xiong, Grace Y. Yi

The R Journal

Though longitudinal data often contain missing responses and error-prone covariates, relatively little work has been available to simultaneously correct for the effects of response missingness and covariate measurement error on analysis of longitudinal data. Yi (2008) proposed a simulation based marginal method to adjust for the bias induced by measurement error in covariates as well as by missingness in response. The proposed method focuses on modeling the marginal mean and variance structures, and the missing at random mechanism is assumed. Furthermore, the distribution of covariates are left unspecified. These features make the proposed method applicable to a broad settings. In …

Simcorrmix: Simulation Of Correlated Data With Multiple Variable Types Including Continuous And Count Mixture Distributions, Allison Fialkowski, Hemant Tiwari

The R Journal

The SimCorrMix package generates correlated continuous (normal, non-normal, and mixture), binary, ordinal, and count (regular and zero-inflated, Poisson and Negative Binomial) variables that mimic real-world data sets. Continuous variables are simulated using either Fleishman’s third-order or Headrick’s fifth-order power method transformation. Simulation occurs at the component level for continuous mixture distributions, and the target correlation matrix is specified in terms of correlations with components. However, the package contains functions to approximate expected correlations with continuous mixture variables. There are two simulation pathways which calculate intermediate correlations involving count variables differently, increasing accuracy under a wide range of parameters. The package …

The R Journal (June 2019) 11(1): Complete Issue, The R Foundation

The R Journal

Editorial, Michael J. Kane

Contributed Research Articles

atable: Create Tables for Clinical Trial Reports, Armin Ströbel

Connecting R with D3 for Dynamic Graphics, to Explore Multivariate Data with Tours, Michael Kipp, Ursula Laa, and Dianne Cook

Optimization Routines for Enforcing One-to-One Matches in Record Linkage Problems, Diego Moretti, Luca Valentino, and Tiziana Tuoto

mixedsde: A Package to Fit Mixed Stochastic Differential Equations, Charlotte Dion, Simone Hermann, and Adeline Samson

Indoor Positioning and Fingerprinting: The R Package ipft, Emilio Sansano, Raúl Montoliu, Óscar Belmonte, and Joaquín Torres-Sospedra

RobustGaSP: Robust Gaussian Stochastic Process Emulation in R, Mengyang Gu, Jesus Palomo, and James …

Meta-Transfer Learning For Few-Shot Learning, Qianru Sun, Yaoyao Liu, Tat-Seng Chua, Bernt Schiele

Research Collection School Of Computing and Information Systems

Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available. As deep neural networks (DNNs) tend to overfit using a few samples only, meta-learning typically uses shallow neural networks (SNNs), thus limiting its effectiveness. In this paper we propose a novel few-shot learning method called meta-transfer learning (MTL) which learns to adapt a deep NN for few shot learning tasks. Specifically, …

Simulating Epidemics And Interventions On High Resolution Social Networks, Christopher E. Siu

Master's Theses

Mathematical models of disease spreading are a key factor of ensuring that we are prepared to deal with the next epidemic. They allow us to predict how an infection will spread throughout a population, thereby allowing us to make intelligent choices when attempting to contain the disease. Whether due to a lack of empirical data, a lack of computational power, a lack of biological understanding, or some combination thereof, traditional models must make sweeping assumptions about the behavior of a population during an epidemic.

In this thesis, we implement granular epidemic simulations using a rich social network constructed from real-world …

Geometric Top-K Processing: Updates Since Mdm'16 [Advanced Seminar], Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

The top-k query has been studied extensively, and is considered the norm for multi-criteria decision making in large databases. In recent years, research has considered several complementary operators to the traditional top-k query, drawing inspiration (both in terms of problem formulation and solution design) from the geometric nature of the top-k processing model. In this seminar, we will present advances in that stream of work, focusing on updates since the preliminary seminar on the same topic in MDM'16.

A Probabilistic Model Of The Bitcoin Blockchain, Marc Jourdan, Sebastien Blandin, Laura Wynter, Pralhad Deshpande

Research Collection School Of Computing and Information Systems

The Bitcoin transaction graph is a public data structure organized as transactions between addresses, each associated with a logical entity. In this work, we introduce a complete probabilistic model of the Bitcoin Blockchain, setting the basis for follow-up AI applications on Bitcoin transactions. We first formulate a set of conditional dependencies induced by the Bitcoin protocol at the block level and derive a corresponding fully observed graphical model of a Bitcoin block. We then extend the model to include hidden entity attributes such as the functional category of the associated logical agent and derive asymptotic bounds on the privacy properties …

Learning Cross-Modal Embeddings With Adversarial Networks For Cooking Recipes And Food Images, Hao Wang, Doyen Sahoo, Chenghao Liu, Ee-Peng Lim, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Food computing is playing an increasingly important role in human daily life, and has found tremendous applications in guiding human behavior towards smart food consumption and healthy lifestyle. An important task under the food-computing umbrella is retrieval, which is particularly helpful for health related applications, where we are interested in retrieving important information about food (e.g., ingredients, nutrition, etc.). In this paper, we investigate an open research task of cross-modal retrieval between cooking recipes and food images, and propose a novel framework Adversarial Cross-Modal Embedding (ACME) to resolve the cross-modal retrieval task in food domains. Specifically, the goal is to …

View, Like, Comment, Post: Analyzing User Engagement By Topic At 4 Levels Across 5 Social Media Platforms For 53 News Organizations, Kholoud K. Aldous, Jisun An, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

We evaluate the effects of the topics of social media posts on audiences across five social media platforms (i.e., Facebook, Instagram, Twitter, YouTube, and Reddit) at four levels of user engagement. We collected 3,163,373 social posts from 53 news organizations across five platforms during an 8month period. We analyzed the differences in news organization platform strategies by focusing on topic variations by organization and the corresponding effect on user engagement at four levels. Findings show that topic distribution varies by platform, although there are some topics that are popular across most platforms. User engagement levels vary both by topics and …

Lightweight Privacy-Preserving Ensemble Classification For Face Recognition, Zhuo Ma, Yang Liu, Ximeng Liu, Jianfeng Ma, Kui Ren

Research Collection School Of Computing and Information Systems

The development of machine learning technology and visual sensors is promoting the wider applications of face recognition into our daily life. However, if the face features in the servers are abused by the adversary, our privacy and wealth can be faced with great threat. Many security experts have pointed out that, by 3-D-printing technology, the adversary can utilize the leaked face feature data to masquerade others and break the E-bank accounts. Therefore, in this paper, we propose a lightweight privacy-preserving adaptive boosting (AdaBoost) classification framework for face recognition (POR) based on the additive secret sharing and edge computing. First, we …

Classifying Challenging Behaviors In Autism Spectrum Disorder With Neural Document Embeddings, Abigail Atchison

Computational and Data Sciences (MS) Theses

The understanding and treatment of challenging behaviors in individuals with Autism Spectrum Disorder is paramount to enabling the success of behavioral therapy; an essential step in this process being the labeling of challenging behaviors demonstrated in therapy sessions. These manifestations differ across individuals and within individuals over time and thus, the appropriate classification of a challenging behavior when considering purely qualitative factors can be unclear. In this thesis we seek to add quantitative depth to this otherwise qualitative task of challenging behavior classification. We do so through the application of natural language processing techniques to behavioral descriptions extracted from the …

Real-Time Rfi Mitigation In Radio Astronomy, Emily Ramey, Nick Joslyn, Richard Prestage, Michael Lam, Luke Hawkins, Tim Blattner, Mark Whitehead

Senior Honors Papers / Undergraduate Theses

As the use of wireless technology has increased around the world, Radio Frequency Interference (RFI) has become more and more of a problem for radio astronomers. Preventative measures exist to limit the presence of RFI, and programs exist to remove it from saved data, but the use of algorithms to detect and remove RFI as an observation is occurring is much less common. Such a method would be incredibly useful for observations in which the data must undergo several rounds of processing before being saved, as in pulsar timing studies. Strategies for real-time mitigation have been discussed and tested with …

Arecibo Message, Joshua P. Tan

Open Educational Resources

This two week assignment asks students to interpret and analyze the 1974 Arecibo Message sent by Drake and Sagan. Week 1 introduces the concepts behind the construction of the message and engages with a critical analysis of the architecture and the contents of the message. Week 2 asks students to develop software in a Jupyter Notebook (available for free from the Anaconda Python Distribution) to interpret messages that were similar to those produced by Drake and Sagan.

The Fluid Representations Of Networks Estimating Liquid Viscosity, Jan Jaap R. Van Assen, Shin'ya Nishida, Roland W. Fleming

MODVIS Workshop

No abstract provided.

Simplicity Diffexpress: A Bespoke Cloud-Based Interface For Rna-Seq Differential Expression Modeling And Analysis, Cintia C. Palu, Marcelo Ribeiro-Alves, Yanxin Wu, Brendan Lawlor, Pavel V. Baranov, Brian Kelly, Paul Walsh

Department of Computer Science Publications

One of the key challenges for transcriptomics-based research is not only the processing of large data but also modeling the complexity of features that are sources of variation across samples, which is required for an accurate statistical analysis. Therefore, our goal is to foster access for wet lab researchers to bioinformatics tools, in order to enhance their ability to explore biological aspects and validate hypotheses with robust analysis. In this context, user-friendly interfaces can enable researchers to apply computational biology methods without requiring bioinformatics expertise. Such bespoke platforms can improve the quality of the findings by allowing the researcher to …

Fluid Transport In Porous Media For Engineering Applications, Eric M. Benner

Chemical and Biological Engineering ETDs

This doctoral dissertation presents three topics in modeling fluid transport through porous media used in engineering applications. The results provide insights into the design of fuel cell components, catalyst and drug delivery particles, and aluminum- based materials. Analytical and computational methods are utilized for the modeling of the systems of interest. Theoretical analysis of capillary-driven transport in porous media show that both geometric and evaporation effects significantly change the time dependent behavior of liquid imbibition and give a steady state flux into the medium. The evaporation–capillary number is significant in determining the time-dependent behavior of capillary flows in porous media. …

Depressiongnn: Depression Prediction Using Graph Neural Network On Smartphone And Wearable Sensors, Param Bidja

Honors Scholar Theses

Depression prediction is a complicated classification problem because depression diagnosis involves many different social, physical, and mental signals. Traditional classification algorithms can only reach an accuracy of no more than 70% given the complexities of depression. However, a novel approach using Graph Neural Networks (GNN) can be used to reach over 80% accuracy, if a graph can represent the depression data set to capture differentiating features. Building such a graph requires 1) the definition of node features, which must be highly correlated with depression, and 2) the definition for edge metrics, which must also be highly correlated with depression. In …

The Effects Of Finite Precision On The Simulation Of The Double Pendulum, Rebecca Wild

Senior Honors Projects, 2010-2019

We use mathematics to study physical problems because abstracting the information allows us to better analyze what could happen given any range and combination of parameters. The problem is that for complicated systems mathematical analysis becomes extremely cumbersome. The only effective and reasonable way to study the behavior of such systems is to simulate the event on a computer. However, the fact that the set of floating-point numbers is finite and the fact that they are unevenly distributed over the real number line raises a number of concerns when trying to simulate systems with chaotic behavior. In this research we …

Modeling A Chaotic Billiard: The Bunimovich Stadium, Randal Shoemaker

Senior Honors Projects, 2010-2019

The Bunimovich stadium is a chaotic dynamical system in which a single particle, known as a billiard, moves indefinitely within a barrier without loss of momentum. Mathematicians and physicists have been interested in its properties since it was discovered to be chaotic in the 1970’s [5] [3] [4]. The Bunimovich stadium is actively researched [9]. This thesis and its accompanying software, the Bunimovich Stadia Evolution Viewer (BSEV), present a novel visual representation of the the chaotic dynamical system. The goal for the software is to provide insights into the stadium’s properties to aid researchers. This tool allows one to visualize …

Visualization And Machine Learning Techniques For Nasa’S Em-1 Big Data Problem, Antonio P. Garza Iii, Jose Quinonez, Misael Santana, Nibhrat Lohia

SMU Data Science Review

In this paper, we help NASA solve three Exploration Mission-1 (EM-1) challenges: data storage, computation time, and visualization of complex data. NASA is studying one year of trajectory data to determine available launch opportunities (about 90TBs of data). We improve data storage by introducing a cloud-based solution that provides elasticity and server upgrades. This migration will save $120k in infrastructure costs every four years, and potentially avoid schedule slips. Additionally, it increases computational efficiency by 125%. We further enhance computation via machine learning techniques that use the classic orbital elements to predict valid trajectories. Our machine learning model decreases trajectory …

Automate Nuclei Detection Using Neural Networks, Jonathan Flores, Thejas Prasad, Jordan Kassof, Robert Slater

SMU Data Science Review

Nuclei identification is a pivotal first step in many areas of biomedical research. Pathologists often observe images containing microscopic nuclei as part of their day to day jobs. During research, pathologists must identify nuclei characteristics from microscopic images such as: volume of nuclei, size, density and individual position within image. The pathology field can benefit from image detection enhancements done through the use of computer image segmentation techniques. This research presents methods that can be used to identify all the cell nuclei contained in images. Multiple techniques were experimented with such as edge detection and Convolutional Neural Networks with U-Net …

Powers And Behaviors Of Directed Self-Assembly, Trent Allen Rogers

Graduate Theses and Dissertations

In nature there are a variety of self-assembling systems occurring at varying scales which give rise to incredibly complex behaviors. Theoretical models of self-assembly allow us to gain insight into the fundamental nature of self-assembly independent of the specific physical implementation. In Winfree's abstract tile assembly model (aTAM), the atomic components are unit square "tiles" which have "glues" on their four sides. Beginning from a seed assembly, these tiles attach one at a time during the assembly process in an asynchronous and nondeterministic manner.

We can gain valuable insights into the nature of self-assembly by comparing different models of self-assembly …