Numerical Analysis and Scientific Computing Commons^™

X-Ray Emission Produced In Charge-Exchange Collisions Between Highly Charged Ions And Argon: Role Of The Multiple Electron Capture, Sebastian Otranto, N. D. Cariatore, Ronald E. Olson

Physics Faculty Research & Creative Works

In this work we use the classical trajectory Monte Carlo method within an eight-electron scheme to theoretically study photonic spectra that follow charge-exchange processes between highly charged ions of charge states 10+, 17+, 18+, and 36+ with neutral argon. The energy range considered is 18 eV/amu to 4 keV/amu, covering typical electron beam ion traps and solar wind energies. The role played by multiple electron capture processes for the different collision systems under consideration is explicitly analyzed and its contribution separated as arising from radiative decay and autoionizing multiple capture. For the present collision systems we find that multiple electron …

The R Journal (December 2014) 6(2): Complete Issue, The R Foundation

The R Journal

Editorial, Deepayan Sarkar

Contributed Research Articles

Coordinate-Based Meta-Analysis of fMRI Studies with R, Andrea Stocco

Automatic Conversion of Tables to LongForm Dataframes, Jimmy Oh

Prinsimp, Jonathan Zhang, Nancy Heckman, Davor Cubranic, Joel G. Kingsolver, Travis Gaydos, and J. S. Marron

phaseR: An R Package for Phase Plane Analysis of Autonomous ODE Systems, Michael J. Grayling

Flexible R Functions for Processing Accelerometer Data, with Emphasis on NHANES 2003-2006, Dane R. Van Domelen and W. Stephen Pittard

Applying spartan to Understand Parameter Uncertainty in Simulations, Kieran Alden, Mark Read, Paul S. Andrews, Jon Timmis, and Mark Coles

ngspatial: A Package for Fitting …

Theoretical And Experimental (E, 2e) Study Of Electron-Impact Ionization Of Laser-Aligned Mg Atoms, Sadek Amami, Andrew J. Murray, Al Stauffer, Kate Nixon, Gregory Armstrong, James Colgan, Don H. Madison

Physics Faculty Research & Creative Works

We have performed calculations of the fully differential cross sections for electron-impact ionization of magnesium atoms. Three theoretical approximations, the time-dependent close coupling, the three-body distorted wave, and the distorted wave Born approximation, are compared with experiment in this article. Results will be shown for ionization of the 3s ground state of Mg for both asymmetric and symmetric coplanar geometries. Results will also be shown for ionization of the 3p state which has been excited by a linearly polarized laser which produces a charge cloud aligned perpendicular to the laser beam direction and parallel to the linear polarization. Theoretical and …

Extracting Interest Tags From Twitter User Biographies, Ying Ding, Jing Jiang

Research Collection School Of Computing and Information Systems

Twitter, one of the most popular social media platforms, has been studied from different angles. One of the important sources of information in Twitter is users’ biographies, which are short self-introductions written by users in free form. Biographies often describe users’ background and interests. However, to the best of our knowledge, there has not been much work trying to extract information from Twitter biographies. In this work, we study how to extract information revealing users’ personal interests from Twitter biographies. A sequential labeling model is trained with automatically constructed labeled data. The popular patterns expressing user interests are extracted and …

Data Preparation For Social Network Mining And Analysis, Yazhe Wang

Dissertations and Theses Collection (Open Access)

This dissertation studies the problem of preparing good-quality social network data for data analysis and mining. Modern online social networks such as Twitter, Facebook, and LinkedIn have rapidly grown in popularity. The consequent availability of a wealth of social network data provides an unprecedented opportunity for data analysis and mining researchers to determine useful and actionable information in a wide variety of fields such as social sciences, marketing, management, and security. However, raw social network data are vast, noisy, distributed, and sensitive in nature, which challenge data mining and analysis tasks in storage, efficiency, accuracy, etc. Many mining algorithms cannot …

Issues Of Social Data Analytics With A New Method For Sentiment Analysis Of Social Media Data, Zhaoxia Wang, Victor J. C. Tong, David Chan

Research Collection School of Social Sciences

Social media data consists of feedback, critiques and other comments that are posted online by internet users. Collectively, these comments may reflect sentiments that are sometimes not captured in traditional data collection methods such as administering a survey questionnaire. Thus, social media data offers a rich source of information, which can be adequately analyzed and understood. In this paper, we survey the extant research literature on sentiment analysis and discuss various limitations of the existing analytical methods. A major limitation in the large majority of existing research is the exclusive focus on social media data in the English language. There …

High-Dimensional Data Stream Classification Via Sparse Online Learning, Dayong Wang, Pengcheng Wu, Peilin Zhao, Yue Wu, Chunyan Miao, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

The amount of data in our society has been exploding in the era of big data today. In this paper, we address several open challenges of big data stream classification, including high volume, high velocity, high dimensionality, and high sparsity. Many existing studies in data mining literature solve data stream classification tasks in a batch learning setting, which suffers from poor efficiency and scalability when dealing with big data. To overcome the limitations, this paper investigates an online learning framework for big data stream classification tasks. Unlike some existing online data stream classification techniques that are often based on first-order …

Anomaly Detection Through Enhanced Sentiment Analysis On Social Media Data, Zhaoxia Wang, Victor Joo, Chuan Tong, Xin Xin, Hoong Chor Chin

Research Collection School Of Computing and Information Systems

Anomaly detection in sentiment analysis refers to detecting abnormal opinions, sentiment patterns or special temporal aspects of such patterns in a collection of data. The anomalies detected may be due to sudden sentiment changes hidden in large amounts of text. If these anomalies are undetected or poorly managed, the consequences may be severe, e.g. A business whose customers reveal negative sentiments and will no longer support the establishment. Social media platforms, such as Twitter, provide a vast source of information, which includes user feedback, opinion and information on most issues. Many organizations also leverage social media platforms to publish information …

A Pareto-Frontier Analysis Of Performance Trends For Small Regional Coverage Leo Constellation Systems, Christopher Alan Hinds

Master's Theses

As satellites become smaller, cheaper, and quicker to manufacture, constellation systems will be an increasingly attractive means of meeting mission objectives. Optimizing satellite constellation geometries is therefore a topic of considerable interest. As constellation systems become more achievable, providing coverage to specific regions of the Earth will become more common place. Small countries or companies that are currently unable to afford large and expensive constellation systems will now, or in the near future, be able to afford their own constellation systems to meet their individual requirements for small coverage regions.

The focus of this thesis was to optimize constellation geometries …

Boscor: Extending R From The Desktop To The Grid, Derek J. Weitzel, Jaime Frey, Marco Mambelli, Dan Fraser, Miha Ahronovitz, David Swanson

Holland Computing Center: Faculty Publications

In this paper, we describe a framework to execute R functions on remote resources from the desktop using Bosco. The R language is attractive to researchers because of its high level programming constructs which lower the barrier of entry for use. As the use of the R programming language in HPC and High Throughput Computing (HTC) has grown, so too has the need for parallel libraries in order to utilize computing resources.

Bosco is middleware that uses common protocols to manage job submissions to a variety of remote computational platforms and resources. The researcher is able to control and monitor …

Cubic Spline Interpolation By Solving A Recurrence Equation Instead Of A Tridiagonal Matrix, Peter Revesz

School of Computing: Conference and Workshop Papers

The cubic spline interpolation method is proba- bly the most widely-used polynomial interpolation method for functions of one variable. However, the cubic spline method requires solving a tridiagonal matrix-vector equation with an O(n) computational time complexity where n is the number of data measurements. Even an O(n) time complexity may be too much in some time-ciritical applications, such as continuously estimating and updating the flight paths of moving objects. This paper shows that under certain boundary conditions the tridiagonal matrix solving step of the cubic spline method could be entirely eliminated and instead the coefficients of the unknown cubic polynomials …

Linguistic Analysis Of Toxic Behavior In An Online Video Game, Haewoon Kwak, Telefonica

Research Collection School Of Computing and Information Systems

In this paper we explore the linguistic components of toxic behavior by using crowdsourced data from over 590 thousand cases of accused toxic players in a popular match-based competition game, League of Legends. We perform a series of linguistic analyses to gain a deeper understanding of the role communication plays in the expression of toxic behavior. We characterize linguistic behavior of toxic players and compare it with that of typical players in an online competition game. We also find empirical support describing how a player transitions from typical to toxic behavior. Our findings can be helpful to automatically detect and …

Large-Scale Mechanical Buckle Fold Development And The Initiation Of Tensile Fractures, Andreas Eckert, Peter Connolly, Xiaolong Liu

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Tensile failure associated with buckle folding is commonly associated to the distribution of outer arc extension but has also been observed on fold limbs. This study investigates whether tensile stresses and associated failure can be explained by the process of buckling under realistic in situ stress conditions. A 2-D plane strain finite element modeling approach is used to study single-layer buckle folds with a Maxwell viscoelastic rheology. A variety of material parameters are considered and their influence on the initiation of tensile stresses during the various stages of deformation is analyzed. It is concluded that the buckling process determines the …

Estimating The Flight Path Of Moving Objects Based On Acceleration Data, Peter Revesz

School of Computing: Conference and Workshop Papers

Inertial navigation is the problem of estimating the flight path of a moving object based on only acceleration measurements. This paper describes and compares two approaches for inertial navigation. Both approaches estimate the flight path of the moving object using cubic spline interpolation, but they find the coefficients of the cubic spline pieces by different methods. The first approach uses a tridiagonal matrix, while the second approach uses recurrence equations. They also require different boundary conditions. While both approaches work in O(n) time where n is the number of given acceleration measurements, the recurrence equation-based method can be easier updated …

Online Passive Aggressive Active Learning And Its Applications, Jing Lu, Peilin Zhao, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

We investigate online active learning techniques for classification tasks in data stream mining applications. Unlike traditional learning approaches (either batch or online learning) that often require to request the class label of each incoming instance, online active learning queries only a subset of informative incoming instances to update the classification model, which aims to maximize classification performance using minimal human labeling effort during the entire online stream data mining task. In this paper, we present a new family of algorithms for online active learning called Passive-Aggressive Active (PAA) learning algorithms by adapting the popular Passive-Aggressive algorithms in an online active …

Generative Modeling Of Entity Comparisons In Text, Maksim Tkachenko, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Users frequently rely on online reviews for decision making. In addition to allowing users to evaluate the quality of individual products, reviews also support comparison shopping. One key user activity is to compare two (or more) products based on a specific aspect. However, making a comparison across two different reviews, written by different authors, is not always equitable due to the different standards and preferences of individual authors. Therefore, we focus instead on comparative sentences, whereby two products are compared directly by a review author within a single sentence. We study the problem of comparative relation mining. Given a set …

Dynamic Clustering Of Contextual Multi-Armed Bandits, Trong T. Nguyen, Hady W. Lauw

Research Collection School Of Computing and Information Systems

With the prevalence of the Web and social media, users increasingly express their preferences online. In learning these preferences, recommender systems need to balance the trade-off between exploitation, by providing users with more of the "same", and exploration, by providing users with something "new" so as to expand the systems' knowledge. Multi-armed bandit (MAB) is a framework to balance this trade-off. Most of the previous work in MAB either models a single bandit for the whole population, or one bandit for each user. We propose an algorithm to divide the population of users into multiple clusters, and to customize the …

Modloc: Localizing Multiple Objects In Dynamic Indoor Environment, Xiaonan Guo, Dian Zhang, Kaishun Wu, Lionel M. Ni

Research Collection School Of Computing and Information Systems

Radio frequency (RF) based technologies play an important role in indoor localization, since Radio Signal Strength (RSS) can be easily measured by various wireless devices without additional cost. Among these, radio map based technologies (also referred as fingerprinting technologies) are attractive due to high accuracy and easy deployment. However, these technologies have not been extensively applied on real environment for two fatal limitations. First, it is hard to localize multiple objects. When the number of target objects is unknown, constructing a radio map of multiple objects is almost impossible. Second, environment changes will generate different multipath signals and severely disturb …

A First Look At Global News Coverage Of Disasters By Using The Gdelt Dataset, Haewoon Kwak, Jisun. An

Research Collection School Of Computing and Information Systems

In this work, we reveal the structure of global news coverage of disasters and its determinants by using a large-scale news coverage dataset collected by the GDELT (Global Data on Events, Location, and Tone) project that monitors news media in over 100 languages from the whole world. Significant variables in our hierarchical (mixed-effect) regression model, such as population, political stability, damage, and more, are well aligned with a series of previous research. However, we find strong regionalism in news geography, highlighting the necessity of comprehensive datasets for the study of global news coverage.

Networked Employment Discrimination, Tamara Kneese

Media Studies

Employers often struggle to assess qualified applicants, particularly in contexts where they receive hundreds of applications for job openings. In an effort to increase efficiency and improve the process, many have begun employing new tools to sift through these applications, looking for signals that a candidate is “the best fit.” Some companies use tools that offer algorithmic assessments of workforce data to identify the variables that lead to stronger employee performance, or to high employee attrition rates, while others turn to third party ranking services to identify the top applicants in a labor pool. Still others eschew automated systems, but …