Numerical Analysis and Scientific Computing Commons^™

Gmdh: An R Package For Short Term Forecasting Via Gmdh-Type Neural Network Algorithms, Osman Dag, Ceylan Yozgatligil

The R Journal

Group Method of Data Handling (GMDH)-type neural network algorithms are the heuristic self organization method for the modelling of complex systems. GMDH algorithms are utilized for a variety of purposes, examples include identification of physical laws, the extrapolation of physical fields, pattern recognition, clustering, the approximation of multidimensional processes, forecasting without models, etc. In this study, the R package GMDH is presented to make short term forecasting through GMDH-type neural network algorithms. The GMDH package has options to use different transfer functions (sigmoid, radial basis, polynomial, and tangent functions) simultaneously or separately. Data on cancer death rate of Pennsylvania from …

The R Journal (August 2016) 8(1): Complete Issue, The R Foundation

The R Journal

Editorial, Michael Lawrence

Contributed Research Articles

metaplus: An R Package for the Analysis of Robust Meta-Analysis and Meta-Regression, Ken J. Beath

Gender Prediction Methods Based on First Names with genderizeR, Kamil Wais

Conditional Fractional Gaussian Fields with the Package FieldSim, Alexandre Brouste, Jacques Istas, and Sophie Lambert-Lacroix

rTableICC: An R Package for Random Generation of 22K and RC Contingency Tables, Haydar Demirhan

Maps, Coordinate Reference Systems and Visualising Geographic Data with mapmisc, Patrick E. Brown

Variable Clustering in High-Dimensional Linear Regression: The R Package clere, Loïc Yengo, Julien Jacques, Christophe Biernacki, and Mickael Canouil

Stylometry with R: A Package for …

Ε-Kernel Coresets For Stochastic Points, Haitao Wang, Lingxiao Huang, Jian Li, Jeff Mark Phillips

Computer Science Faculty and Staff Publications

With the dramatic growth in the number of application domains that generate probabilistic, noisy and uncertain data, there has been an increasing interest in designing algorithms for geometric or combinatorial optimization problems over such data. In this paper, we initiate the study of constructing epsilon-kernel coresets for uncertain points. We consider uncertainty in the existential model where each point's location is fixed but only occurs with a certain probability, and the locational model where each point has a probability distribution describing its location. An epsilon-kernel coreset approximates the width of a point set in any direction. We consider approximating the …

Comparison Of Experimental And Theoretical Fully Differential Cross Sections For Single Ionization Of The 2s And 2p States Of Li By O⁸⁺ Ions, Ebrahim Ghanbari-Adivi, Daniel Fischer, Natalia Ferreira, Johannes Goullon, Renate Hubele, Aaron Laforge, Michael Schulz, Don H. Madison

Physics Faculty Research & Creative Works

This paper presents a full three-dimensional (3D) comparison between experiment and theory for 24 MeV O⁸⁺ single ionization of the 2s ground state of lithium and the 2p excited state. Two theoretical approximations are examined: the three-body continuum distorted-wave (3DW) and three-body continuum distorted-wave-eikonal initial state (3DW-EIS). Normally, there is a significant difference between these two approaches and the 3DW-EIS is in much better agreement with experiment. In this case, there is very little difference between the two approaches and both are in very good agreement with experiment. For the excited 2p state, the 3D cross sections would exhibit …

Contact Process With Temporal Disorder, Hatem Barghathi, Thomas Vojta, José A. Hoyos

Physics Faculty Research & Creative Works

We investigate the influence of time-varying environmental noise, i.e., temporal disorder, on the nonequilibrium phase transition of the contact process. Combining a real-time renormalization group, scaling theory, and large scale Monte-Carlo simulations in one and two dimensions, we show that the temporal disorder gives rise to an exotic critical point. At criticality, the effective noise amplitude diverges with increasing time scale, and the probability distribution of the density becomes infinitely broad, even on a logarithmic scale. Moreover, the average density and survival probability decay only logarithmically with time. This infinite-noise critical behavior can be understood as the temporal counterpart of …

Unsupervised Multi-Graph Cross-Modal Hashing For Large-Scale Multimedia Retrieval, Liang Xie, Lei Zhu, Guoqi Chen

Research Collection School Of Computing and Information Systems

With the advance of internet and multimedia technologies, large-scale multi-modal representation techniques such as cross-modal hashing, are increasingly demanded for multimedia retrieval. In cross-modal hashing, three essential problems should be seriously considered. The first is that effective cross-modal relationship should be learned from training data with scarce label information. The second is that appropriate weights should be assigned for different modalities to reflect their importance. The last is the scalability of training process which is usually ignored by previous methods. In this paper, we propose Multi-graph Cross-modal Hashing (MGCMH) by comprehensively considering these three points. MGCMH is unsupervised method which …

Probabilistic Robust Route Recovery With Spatio-Temporal Dynamics, Hao Wu, Jiangyun Mao, Weiwei Sun, Baihua Zheng, Hanyuan Zhang, Ziyang Chen, Wei Wang

Research Collection School Of Computing and Information Systems

Vehicle trajectories are one of the most important data in location-based services. The quality of trajectories directly affects the services. However, in the real applications, trajectory data are not always sampled densely. In this paper, we study the problem of recovering the entire route between two distant consecutive locations in a trajectory. Most existing works solve the problem without using those informative historical data or solve it in an empirical way. We claim that a data-driven and probabilistic approach is actually more suitable as long as data sparsity can be well handled. We propose a novel route recovery system in …

An Algorithm For The Machine Calculation Of Minimal Paths, Robert Whitinger

Electronic Theses and Dissertations

Problems involving the minimization of functionals date back to antiquity. The mathematics of the calculus of variations has provided a framework for the analytical solution of a limited class of such problems. This paper describes a numerical approximation technique for obtaining machine solutions to minimal path problems. It is shown that this technique is applicable not only to the common case of finding geodesics on parameterized surfaces in R³, but also to the general case of finding minimal functionals on hypersurfaces in Rⁿ associated with an arbitrary metric.

Singapore In Motion: Insights On Public Transport Service Level Through Farecard And Mobile Data Analytics, Hasan Poonawala, Vinay Kolar, Sebastien Blandin, Laura Wynter, Sambit Sahu

Research Collection School Of Computing and Information Systems

Given the changing dynamics of mobility patterns and rapid growth of cities, transport agencies seek to respond more rapidly to needs of the public with the goal of offering an effective and competitive public transport system. A more data-centric approach for transport planning is part of the evolution of this process. In particular, the vast penetration of mobile phones provides an opportunity to monitor and derive insights on transport usage. Real time and historical analyses of such data can give a detailed understanding of mobility patterns of people and also suggest improvements to current transit systems. On its own, however, …

Machine Learning Methods For Brain Image Analysis, Ahmed Fakhry

Computer Science Theses & Dissertations

Understanding how the brain functions and quantifying compound interactions between complex synaptic networks inside the brain remain some of the most challenging problems in neuroscience. Lack or abundance of data, shortage of manpower along with heterogeneity of data following from various species all served as an added complexity to the already perplexing problem. The ability to process vast amount of brain data need to be performed automatically, yet with an accuracy close to manual human-level performance. These automated methods essentially need to generalize well to be able to accommodate data from different species. Also, novel approaches and techniques are becoming …

Novel Monte Carlo Methods For Large-Scale Linear Algebra Operations, Hao Ji

Computer Science Theses & Dissertations

Linear algebra operations play an important role in scientific computing and data analysis. With increasing data volume and complexity in the "Big Data" era, linear algebra operations are important tools to process massive datasets. On one hand, the advent of modern high-performance computing architectures with increasing computing power has greatly enhanced our capability to deal with a large volume of data. One the other hand, many classical, deterministic numerical linear algebra algorithms have difficulty to scale to handle large data sets.

Monte Carlo methods, which are based on statistical sampling, exhibit many attractive properties in dealing with large volume of …

The First Potential Energy Surfaces For The C₆Hˉ-H₂ And C₆Hˉ-He Collisional Systems And Their Corresponding Inelastic Cross Sections, Kyle M. Walker, Fabien Dumouchel, François Lique, Richard Dawes

Chemistry Faculty Research & Creative Works

Molecular anions have recently been detected in the interstellar and circumstellar media. Accurate modeling of their abundance requires calculations of collisional data with the most abundant species that are usually He atoms and H₂ molecules. In this paper, we focus on the collisional excitation of the first observed molecular anion, C₆H^-, by He and H₂. Theoretical calculations of collisional cross sections rely generally on ab initio interaction potential energy surfaces (PESs). Hence, we present here the first PESs for the C₆H^--H₂ and C₆H^--He van …

Multiple Fermi Surfaces In Superconducting Nb-Doped Bi₂Se₃, B. J. Lawson, Paul Corbae, Gang Li, Fan Yu, Tomoya Asaba, Colin Tinsman, Y. Qiu, Julia E. Medvedeva, Yew San Hor, L. Li

Physics Faculty Research & Creative Works

Topological insulator Bi₂Se₃ has shown a number of interesting physical properties. Doping Bi₂Se₃ with copper or strontium has been demonstrated to make the material superconducting and potentially even a topological superconductor. The recent discovery of superconducting niobium-doped Bi₂Se₃ reveals an exciting new physical phenomenon, the coexistence of superconductivity and magnetic ordering, as well as signatures of an odd-parity p-wave superconducting order. To understand this new phenomenon, a detailed knowledge of the electronic structure is needed. We present an observation of quantum oscillations in the magnetization (the de Haas-van Alphen effect) of …

A Step Towards An Intelligent Digital Training Management System (I-Dtms), Andrew Dugger

Theses and Dissertations

The U.S. Army Digital Training Management System (DTMS) is a web-based system designed to create a single point of entry for units to schedule unit training, manage training resources, and create schedules and master calendars for training. Currently, the U.S. Army uses DTMS to manage unit training and help commanders at each step of the training management process from planning and preparing to execute and assessing the training plans. This research aims to add intelligent features to DTMS through augmenting it with an intelligent decision support system (ITPSS) that utilizes artificial intelligence techniques (case-based reasoning, in particular) to determine if …

Spica: Stereographic Projection For Interactive Crystallographic Analysis, Xingzhong Li

Nebraska Center for Materials and Nanoscience: Faculty Publications

In numerous research fields, especially the applications of electron and X-ray diffraction, stereographic projection represents a powerful tool for researchers. SPICA is a new computer program for stereographic projection in interactive crystallographic analysis, which inherits features from the previous JECP/SP and includes more functions for extensive crystallographic analysis. SPICA provides fully interactive options for users to plot stereograms of crystal directions and crystal planes, traces, and Kikuchi maps for an arbitrary crystal structure; it can be used to explore the orientation relationships between two crystalline phases with a composite stereogram; it is also used to predict the tilt angles of …

Where Is The Goldmine? Finding Promising Business Locations Through Facebook Data Analytics, Jovian Lin, Richard Oentaryo, Ee-Peng Lim, Casey Vu, Adrian Vu, Agus Kwee

Research Collection School Of Computing and Information Systems

If you were to open your own cafe, would you not want to effortlessly identify the most suitable location to set up your shop? Choosing an optimal physical location is a critical decision for numerous businesses, as many factors contribute to the final choice of the location. In this paper, we seek to address the issue by investigating the use of publicly available Facebook Pages data-which include user "check-ins", types of business, and business locations-to evaluate a user-selected physical location with respect to a type of business. Using a dataset of 20,877 food businesses in Singapore, we conduct analysis of …

Word Clouds With Latent Variable Analysis For Visual Comparison Of Documents, Tuan M. V. Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Word cloud is a visualization form for text that is recognized for its aesthetic, social, and analytical values. Here, we are concerned with deepening its analytical value for visual comparison of documents. To aid comparative analysis of two or more documents, users need to be able to perceive similarities and differences among documents through their word clouds. However, as we are dealing with text, approaches that treat words independently may impede accurate discernment of similarities among word clouds containing different words of related meanings. We therefore motivate the principle of displaying related words in a coherent manner, and propose to …

Energy Consumption Prediction With Big Data: Balancing Prediction Accuracy And Computational Resources, Katarina Grolinger, Miriam Am Capretz, Luke Seewald

Electrical and Computer Engineering Publications

In recent years, advances in sensor technologies and expansion of smart meters have resulted in massive growth of energy data sets. These Big Data have created new opportunities for energy prediction, but at the same time, they impose new challenges for traditional technologies. On the other hand, new approaches for handling and processing these Big Data have emerged, such as MapReduce, Spark, Storm, and Oxdata H2O. This paper explores how findings from machine learning with Big Data can benefit energy consumption prediction. An approach based on local learning with support vector regression (SVR) is presented. Although local learning itself is …

Optimizing The Mix Of Games And Their Locations On The Casino Floor, Jason D. Fiege, Anastasia D. Baran

International Conference on Gambling & Risk Taking

We present a mathematical framework and computational approach that aims to optimize the mix and locations of slot machine types and denominations, plus other games to maximize the overall performance of the gaming floor. This problem belongs to a larger class of spatial resource optimization problems, concerned with optimizing the allocation and spatial distribution of finite resources, subject to various constraints. We introduce a powerful multi-objective evolutionary optimization and data-modelling platform, developed by the presenter since 2002, and show how this software can be used for casino floor optimization. We begin by extending a linear formulation of the casino floor …

Stationary And Time-Dependent Optimization Of The Casino Floor Slot Machine Mix, Anastasia D. Baran, Jason D. Fiege

International Conference on Gambling & Risk Taking

Modeling and optimizing the performance of a mix of slot machines on a gaming floor can be addressed at various levels of coarseness, and may or may not consider time-dependent trends. For example, a model might consider only time-averaged, aggregate data for all machines of a given type; time-dependent aggregate data; time-averaged data for individual machines; or fully time dependent data for individual machines. Fine-grained, time-dependent data for individual machines offers the most potential for detailed analysis and improvements to the casino floor performance, but also suffers the greatest amount of statistical noise. We present a theoretical analysis of single …