Numerical Analysis and Scientific Computing Commons^™

Afmtoolkit: An R Package For Automated Afm Force-Distance Curves Analysis, Rafael Benítez, Vicente J. Bolós, José-Luis Toca-Herrera

The R Journal

Atomic force microscopy (AFM) is widely used to measure molecular and colloidal inter actions as well as mechanical properties of biomaterials. In this paper the afmToolkit R package is introduced. This package allows the user to automatically batch process AFM force-distance and force-time curves. afmToolkit capabilities range from importing ASCII files and preprocessing the curves (contact point detection, baseline correction...) for finding relevant physical information, such as Young’s modulus, adhesion energies and exponential decay for force relaxation and creep experiments. This package also contains plotting, summary and feature extraction functions. The package also comes with several data sets so the …

Learest: Length And Area Estimation From Data Measured With Additive Error, Mirta Benšić, Petar Taler, Safet Hamedović, Emmanuel Karlo Nyarko, Kristian Sabo

The R Journal

This paper describes an R package LeArEst that can be used for estimating object dimensions from a noisy image. The package is based on a simple parametric model for data that are drawn from uniform distribution contaminated by an additive error. Our package is able to estimate the length of the object of interest on a given straight line that intersects it, as well as to estimate the object area when it is elliptically shaped. The input data may be a numerical vector or an image in JPEG format. In this paper, background statistical models and methods for the package …

Manlymix: An R Package For Manly Mixture Modeling, Xuwen Zhu, Volodymyr Melnykov

The R Journal

Model-based clustering is a popular technique for grouping objects based on a finite mixture model. It has countless applications in different fields of study. The R package ManlyMix implements the Manly mixture model that allows modeling skewness within data groups and performs cluster analysis. ManlyMix is a powerful diagnostics tool that is capable of conducting investigation concerning the normality of variables upon fitting of a Manly forward or backward model. Theoretical foundations as well as description of functions are provided. All features of the package are illustrated with examples in great detail. The analysis of real-life datasets demonstrates the flexibility …

Rpsftm: An R Package For Rank Preserving Structural Failure Time Models, Annabel Allison, Ian R. White, Simon Bond

The R Journal

Treatment switching in a randomised controlled trial occurs when participants change from their randomised treatment to the other trial treatment during the study. Failure to account for treatment switching in the analysis (i.e. by performing a standard intention-to-treat analysis) can lead to biased estimates of treatment efficacy. The rank preserving structural failure time model (RPSFTM) is a method used to adjust for treatment switching in trials with survival outcomes. The RPSFTM is due to Robins and Tsiatis (1991) and has been developed by White et al. (1997, 1999).

The method is randomisation based and uses only the randomised treatment group, …

Simulating Probabilistic Long-Term Effects In Models With Temporal Dependence, Christopher Gandrud, Laron K. Williams

The R Journal

The R package pltesim calculates and depicts probabilistic long-term effects in binary models with temporal dependence variables. The package performs two tasks. First, it calculates the change in the probability of the event occurring given a change in a theoretical variable. Second, it calculates the rolling difference in the future probability of the event for two scenarios: one where the event occurred at a given time and one where the event does not occur. The package is consistent with the recent movement to depict meaningful and easy-to-interpret quantities of interest with the requisite measures of uncertainty. It is the first …

Conference Report: R In Insurance 2017, Nicolas Baradel, Christophe Dutang, Caroline Hillairet

The R Journal

The fifth R in Insurance conference took place at Ecole Nationale de la Statistique et de l’Administration Economique (ENSAE, one of the leading French graduate schools in the fields of statistics, economics, finance and actuarial science) Paris on 8 June 2017. This one-day conference focused once more on the wide range of applications of R in insurance, actuarial science and beyond. The conference programme covered topics including reserving, pricing, loss modelling, the use of R in a production environment and also new statistical methods such as big data analysis.

Bayesian Regression Models For Interval-Censored Data In R, Clifford Anderson-Bergman

The R Journal

The package icenReg provides classic survival regression models for interval-censored data. We present an update to the package that extends the parametric models into the Bayesian framework. Core additions include functionality to define the regression model with the standard regression syntax while providing a custom prior function. Several other utility functions are presented that allow for simplified examination of the posterior distribution.

Simulating Noisy, Nonparametric, And Multivariate Discrete Patterns, Ruby Sharma, Sajal Kumar, Hua Zhong, Mingzhou Song

The R Journal

Requiring no analytical forms, nonparametric discrete patterns are flexible in representing complex relationships among random variables. This makes them increasingly useful for data-driven applications. However, there appears to be no software tools for simulating nonparametric discrete patterns, which prevents objective evaluation of statistical methods that discover discrete relationships from data. We present a simulator to generate nonparametric discrete functions as contingency tables. User can request strictly many-to-one functional patterns. The simulator can also produce contingency tables representing dependent non-functional and independent relationships. An option is provided to apply random noise to contingency tables. We demonstrate the utility of the simulator …

Conference Report: User!2017, Tobias Verbeke

The R Journal

After a very successful 2016 edition in Stanford (US), the useR conference invited the R communitytomeetfromJuly4toJuly7inBrussels(Belgium), heart of Europe. The response was extraordinary: 1175 people (of 54 nationalities) travelled the globe to join for a week of intense exchange and discussion. The conference was held in the Wild Gallery which was– for the occasion– the exclusive territory of R aficionados with many co-hosted events including DSC 2017, RIOT 2017 and an R Foundation meeting.

An important theme throughout the conference was to be welcoming and inclusive. In this respect 25 diversity scholarships were awarded and newbies were welcomed at a …

Rentrez: An R Package For The Ncbi Eutils Api, David J. Winter

The R Journal

The USA National Center for Biotechnology Information (NCBI) is one of the world’s most important sources of biological information. NCBI databases like PubMed and GenBank contain mil lions of records describing bibliographic, genetic, genomic, and medical data. Here I present rentrez, a package which provides an R interface to 50 NCBI databases. The package is well-documented, contains an extensive suite of unit tests and has an active user base. The programmatic interface to the NCBI provided by rentrez allows researchers to query databases and download or import particular records into R sessions for subsequent analysis. The complete nature of …

Crtgeedr: An R Package For Doubly Robust Generalized Estimating Equations Estimations In Cluster Randomized Trials With Missing Data, Melanie Prague, Rui Wang, Victor De Gruttola

The R Journal

Semi-parametric approaches based on generalized estimating equations (GEE) are widely used to analyze correlated outcomes in longitudinal settings. In this paper, we present a package CRTgeeDR developed for cluster randomized trials with missing data (CRTs). For use of inverse probability weighting to adjust for missing data in cluster randomized trials, we show that other software lead to biased estimation for non-independence working correlation structure. CRTgeeDR solves this problem. We also extend the ability of existing packages to allow augmented Doubly Robust GEEestimation (DR). Simulation studies demonstrate the consistency of estimators implemented in CRTgeeDR compared to packages such as geepack and …

Bayesbd: An R Package For Bayesian Inference On Image Boundaries, Nicholas Syring, Meng Li

The R Journal

Wepresent the BayesBD package providing Bayesian inference for boundaries of noisy images. The BayesBD package implements flexible Gaussian process priors indexed by the circle to recover the boundary in a binary or Gaussian noised image. The boundary recovered by BayesBD has the practical advantages of guaranteed geometric restrictions and convenient joint inferences under certain assumptions, in addition to its desirable theoretical property of achieving (nearly) minimax optimal rate in a way that is adaptive to the unknown smoothness. The core sampling tasks for our model have linear complexity, and are implemented in C++ for computational efficiency using packages Rcpp and …

Adegraphics: An S4 Lattice-Based Package For The Representation Of Multivariate Data, Aurélie Siberchicot, Alice Julien-Laferrière, Anne-Béatrice Dufour, Jean Thioulouse, Stéphane Dray

The R Journal

The ade4 package provides tools for multivariate analyses. Whereas new statistical methods have been added regularly in the package since its first release in 2002, the graphical functions, that are used to display the main outputs of an analysis, have not benefited from such enhancements. In this context, the adegraphics package, available on CRAN since 2015, is a complete reimplementation of the ade4 graphical functionalities but with large improvements. The package uses the S4 object system (each graph is an object) and is based on the graphical framework provided by lattice and grid. We give a brief description of the …

Arulesviz: Interactive Visualization Of Association Rules With R, Michael Hahsler

The R Journal

Association rule mining is a popular data mining method to discover interesting relation ships between variables in large databases. An extensive toolbox is available in the R-extension package arules. However, mining association rules often results in a vast number of found rules, leaving the analyst with the task to go through a large set of rules to identify interesting ones. Sifting manually through extensive sets of rules is time-consuming and strenuous. Visualization and especially interactive visualization has a long history of making large amounts of data better accessible. The R-extension package arulesViz provides most popular visualization techniques for association …

Queueing: A Package For Analysis Of Queueing Networks And Models In R, Pedro Cañadilla Jiménez, Yolanda Román Montoya

The R Journal

queueing is a package that solves and provides the main performance measures for both basic Markovian queueing models and single and multiclass product-form queueing networks. It can be used both in education and for professional purposes. It provides an intuitive, straightforward wayto build queueing models using S3 methods. The package solves Markovian models of the form M/M/c/K/M/FCFS, open and closed single class Jackson networks, open and closed multiclass networks and mixed networks. Markovian models are used when both the customer inter-arrival time and the server processing time are exponentially distributed. Queueing network solvers are useful for modelling situations in which …

Glmmtmb Balances Speed And Flexibility Among Packages For Zero-Inflated Generalized Linear Mixed Modeling, Mollie E. Brooks, Kasper Kristensen, Koen J. Van Benthem, Arni Magnusson, Casper W. Berg, Anders Nielsen, Hans J. Skaug, Martin Mächler, Benjamin M. Bolker

The R Journal

Count data can be analyzed using generalized linear mixed models when observations are correlated in ways that require random effects. However, count data are often zero-inflated, containing more zeros than would be expected from the typical error distributions. We present a new package, glmmTMB, and compare it to other R packages that fit zero-inflated mixed models. The glmmTMB package fits many types of GLMMs and extensions, including models with continuously distributed responses, but here we focus on count responses. glmmTMB is faster than glmmADMB, MCMCglmm, and brms, and more flexible than INLA and mgcv for zero-inflated …

Process Models Discovery And Traces Classification: A Fuzzy-Bpmn Mining Approach., Kingsley Okoye Dr, Usman Naeem Dr, Syed Islam Dr, Abdel-Rahman H. Tawil Dr, Elyes Lamine Dr

Journal of International Technology and Information Management

The discovery of useful or worthwhile process models must be performed with due regards to the transformation that needs to be achieved. The blend of the data representations (i.e data mining) and process modelling methods, often allied to the field of Process Mining (PM), has proven to be effective in the process analysis of the event logs readily available in many organisations information systems. Moreover, the Process Discovery has been lately seen as the most important and most visible intellectual challenge related to the process mining. The method involves automatic construction of process models from event logs about any domain …

Design And Implementation Of A Stand-Alone Tool For Metabolic Simulations, Milad Ghiasi Rad

School of Computing: Dissertations, Theses, and Student Research

In this thesis, we present the design and implementation of a stand-alone tool for metabolic simulations. This system is able to integrate custom-built SBML models along with external user’s input information and produces the estimation of any reactants participating in the chain of the reactions in the provided model, e.g., ATP, Glucose, Insulin, for the given duration using numerical analysis and simulations. This tool offers the food intake arguments in the calculations to consider the personalized metabolic characteristics in the simulations. The tool has also been generalized to take into consideration of temporal genomic information and be flexible for simulation …

Study Of Self-Similarity In Brain Data, Jennifer Holst

Student Theses

In the area of computer science, past research has found that the concept of self-similarity is present in local and Internet-based network traffic. This study considers the possibility that data traveling through the neuronal network in the human brain is also self-similar. By analyzing publicly available raw EEG data and estimating its Hurst parameter, we find indications that brain data traffic may in fact be self-similar.

Extracting Implicit Suggestions From Students’ Comments: A Text Analytics Approach, Venky Shankararaman, Swapna Gottipati, Jeff Rongsheng Lin, Sandy Gan

Research Collection School Of Computing and Information Systems

At the end of each course, students are required to give feedback on the course and instructor. This feedback includes quantitative rating using Likert scale and qualitative feedback as comments. Such qualitative feedback can provide valuable insights in helping the instructor enhance the course content and teaching delivery. However, the main challenge in analysing the qualitative feedback is the perceived increase in time and effort needed to manually process the textual comments. In this paper, we provide an automated solution for analysing comments, specifically extracting implicit suggestions from the students’ qualitative feedback comments. The implemented solution leverages existing text mining …

Online Learning With Nonlinear Models, Doyen Sahoo

Dissertations and Theses Collection (Open Access)

Recent years have witnessed the success of two broad categories of machine learning algorithms: (i) Online Learning; and (ii) Learning with nonlinear models. Typical machine learning algorithms assume that the entire data is available prior to the training task. This is often not the case in the real world, where data often arrives sequentially in a stream, or is too large to be stored in memory. To address these challenges, Online Learning techniques evolved as a promising solution to having highly scalable and efficient learning methodologies which could learn from data arriving sequentially. Next, as the real world data exhibited …

A Compact Representation Of Human Actions By Sliding Coordinate Coding, Runwei Ding, Qianru Sun, Mengyuan Liu, Hong Liu

Research Collection School Of Computing and Information Systems

Human action recognition remains challenging in realistic videos, where scale and viewpoint changes make the problem complicated. Many complex models have been developed to overcome these difficulties, while we explore using low-level features and typical classifiers to achieve the state-of-the-art performance. The baseline model of feature encoding for action recognition is bag-of-words model, which has shown high efficiency but ignores the arrangement of local features. Refined methods compensate for this problem by using a large number of co-occurrence descriptors or a concatenation of the local distributions in designed segments. In contrast, this article proposes to encode the relative position of …

Pose Guided Person Image Generation, Liqian Ma, Xu Jia, Qianru Sun, Bernt Schiele, Tinne Tuytelaars, Luc Van Gool

Research Collection School Of Computing and Information Systems

This paper proposes the novel Pose Guided Person Generation Network (PG$^2$) that allows to synthesize person images in arbitrary poses, based on an image of that person and a novel pose. Our generation framework PG^2 utilizes the pose information explicitly and consists of two key stages: pose integration and image refinement. In the first stage the condition image and the target pose are fed into a U-Net-like network to generate an initial but coarse image of the person with the target pose. The second stage then refines the initial and blurry result by training a U-Net-like generator in an adversarial …

Multilingual Sentiment Analysis : From Formal To Informal And Scarce Resource Languages, Siaw Ling Lo, Erik Cambria, Raymond Chiong, David Cornforth

Research Collection School Of Computing and Information Systems

The ability to analyse online user-generated content related to sentiments (e.g., thoughts and opinions) on products or policies has become a de-facto skillset for many companies and organisations. Besides the challenge of understanding formal textual content, it is also necessary to take into consideration the informal and mixed linguistic nature of online social media languages, which are often coupled with localised slang as a way to express ‘true’ feelings. Due to the multilingual nature of social media data, analysis based on a single official language may carry the risk of not capturing the overall sentiment of online content. While efforts …

Leveraging Auxiliary Tasks For Document-Level Cross-Domain Sentiment Classification, Jianfei Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper, we study domain adaptationwith a state-of-the-art hierarchicalneural network for document-level sentimentclassification. We first design a newauxiliary task based on sentiment scoresof domain-independent words. We thenpropose two neural network architecturesto respectively induce document embeddingsand sentence embeddings that workwell for different domains. When thesedocument and sentence embeddings areused for sentiment classification, we findthat with both pseudo and external sentimentlexicons, our proposed methods canperform similarly to or better than severalhighly competitive domain adaptationmethods on a benchmark dataset of productreviews.

Inferring Social Media Users’ Demographics From Profile Pictures: A Face++ Analysis On Twitter Users, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Joni Salminen, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

In this research, we evaluate the applicability of using facial recognition of social media account profile pictures to infer the demographic attributes of gender, race, and age of the account owners leveraging a commercial and well-known image service, specifically Face++. Our goal is to determine the feasibility of this approach for actual system implementation. Using a dataset of approximately 10,000 Twitter profile pictures, we use Face++ to classify this set of images for gender, race, and age. We determine that about 30% of these profile pictures contain identifiable images of people using the current state-of-the-art automated means. We then employ …

Examining The Electrical Excitation, Calcium Signaling, And Mechanical Contraction Cycle In A Heart Cell, Kristen Deetz, Nygel Foster, Darius Leftwich, Chad Meyer, Shalin Patel, Carlos Barajas, Matthias K. Gobbert, Zana Coulibaly

Spora: A Journal of Biomathematics

As the leading cause of death in the United States, heart disease has become a principal concern in modern society. Cardiac arrhythmias can be caused by a dysregulation of calcium dynamics in cardiomyocytes. Calcium dysregulation, however, is not yet fully understood and is not easily predicted; this provides motivation for the subsequent research. Excitation-contraction coupling (ECC) is the process through which cardiomyocytes undergo contraction from an action potential. Calcium induced calcium release (CICR) is the mechanism through which electrical excitation is coupled with mechanical contraction through calcium signaling. The study of the interplay between electrical excitation, calcium signaling, and mechanical …

Perfect Ratings With Negative Comments: Learning From Contradictory Patient Survey Responses, Andrew S. Gallan, Marina Girju, Roxana Girju

Patient Experience Journal

This research explores why patients give perfect domain scores yet provide negative comments on surveys. In order to explore this phenomenon, vendor-supplied in-patient survey data from eleven different hospitals of a major U.S. health care system were utilized. The dataset included survey scores and comments from 56,900 patients, collected from January 2015 through October 2016. Of the total number of responses, 30,485 (54%) contained at least one comment. For our analysis, we use a two-step approach: a quantitative analysis on the domain scores augmented by a qualitative text analysis of patients’ comments. To focus the research, we start by building …

Teaching Stats For Data Science, Daniel Kaplan

Faculty Publications

“Data science” is a useful catchword for methods and concepts original to the field of statistics, but typically being applied to large, multivariate, observational records. Such datasets call for techniques not often part of an introduction to statistics: modeling, consideration of covariates, sophisticated visualization, and causal reasoning. This article re-imagines introductory statistics as an introduction to data science and proposes a sequence of 10 blocks that together compose a suitable course for extracting information from contemporary data. Recent extensions to the mosaic packages for R together with tools from the “tidyverse” provide a concise and readable notation for wrangling, visualization, …

A High Quality, Eulerian 3d Fluid Solver In C++, Lejon Anthony Mcgowan

Computer Science and Software Engineering

Fluids are a part of everyday life, yet are one of the hardest elements to properly render in computer graphics. Water is the most obvious entity when thinking of what a fluid simulation can achieve (and it is indeed the focus of this project), but many other aspects of nature, like fog, clouds, and particle effects. Real-time graphics like video games employ many heuristics to approximate these effects, but large-scale renderers aim to simulate these effects as closely as possible.

In this project, I wish to achieve effects of the latter nature. Using the Eulerian technique of discrete grids, I …