Numerical Analysis and Scientific Computing Commons^™

The Billion Object Platform (Bop): A System To Lower Barriers To Support Big, Streaming, Spatio-Temporal Data Sources, Devika Kakkar, Ben Lewis, David Smiley, Ariel Nunez

Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings

With funding from the Sloan Foundation and Harvard Dataverse, the Harvard Center for Geographic Analysis (CGA) has developed a big spatio-temporal data visualization platform called the Billion Object Platform or "BOP". The goal of the project is to lower barriers for scholars who wish to access large, streaming, spatio-temporal datasets. Since once archived, streaming data gets big fast, and since most GIS systems don't support interactive visualization of millions of objects, a new platform was needed. The BOP is loaded with the latest billion geo-tweets and is fed a real-time stream of about 1 million tweets per day. The ...

Optimizing Spatiotemporal Analysis Using Multidimensional Indexing With Geowave, Richard Fecher, Michael A. Whitby

Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings

The open source software GeoWave bridges the gap between geographic information systems and distributed computing. This is done by preserving locality of multidimensional data when indexing it into a single-dimensional key-value store, using space filling curves. This means that like values in each dimension are stored physically close together in the datastore. We demonstrate the efficiencies and benefits of the GeoWave indexing algorithm to store and query billions of spatiotemporal data points. We show how this indexing strategy can be used to reduce query and processing times by multiple orders of magnitude using publicly available taxi trip data published by ...

Machine Learning Based Protein Sequence To (Un)Structure Mapping And Interaction Prediction, Sumaiya Iqbal

University of New Orleans Theses and Dissertations

Proteins are the fundamental macromolecules within a cell that carry out most of the biological functions. The computational study of protein structure and its functions, using machine learning and data analytics, is elemental in advancing the life-science research due to the fast-growing biological data and the extensive complexities involved in their analyses towards discovering meaningful insights. Mapping of protein’s primary sequence is not only limited to its structure, we extend that to its disordered component known as Intrinsically Disordered Proteins or Regions in proteins (IDPs/IDRs), and hence the involved dynamics, which help us explain complex interaction within a ...

Simulation Of Driven Elastic Spheres In A Newtonian Fluid, Shikhar M. Dwivedi

Electronic Thesis and Dissertation Repository

Simulations help us test various restrictions/assumptions placed on physical systems that would otherwise be difficult to efficiently explore experimentally. For example, the Scallop Theorem, first stated in 1977, places limitations on the propulsion mechanisms available to microscopic objects in fluids. In particular, the theorem states that when the viscous forces in a fluid dominate the inertial forces associated with a physical body, such a physical body cannot generate propulsion by means of reciprocal motion. The focus of this thesis is to firstly, explore an adaptive Multiple-timestep(MTS) scheme for faster molecular dynamics(MD) simulations, and secondly, use hybrid MD-LBM ...

Predictive Power And Validity Of Connectome Predictive Modeling: A Replication And Extension, Michael Wang, Joaquin Goni, Enrico Amico

The Summer Undergraduate Research Fellowship (SURF) Symposium

Neuroimaging, particularly functional magnetic resonance imaging (fMRI), is a rapidly growing research area and has applications ranging from disease classification to understanding neural development. With new advancements in imaging technology, researchers must employ new techniques to accommodate the influx of high resolution data sets. Here, we replicate a new technique: connectome-based predictive modeling (CPM), which constructs a linear predictive model of brain connectivity and behavior. CPM’s advantages over classic machine learning techniques include its relative ease of implementation and transparency compared to “black box” opaqueness and complexity. Is this method efficient, powerful, and reliable in the prediction of behavioral ...

Optimization And Control Of Production Of Graphene, Atharva Hans, Nimish M. Awalgaonkar, Majed Alrefae, Ilias Bilionis, Timothy S. Fisher

The Summer Undergraduate Research Fellowship (SURF) Symposium

Graphene is a 2-dimensional element of high practical importance. Despite its exceptional properties, graphene’s real applications in industrial or commercial products have been limited. There are many methods to produce graphene, but none has been successful in commercializing its production. Roll-to-roll plasma chemical vapor deposition (CVD) is used to manufacture graphene at large scale. In this research, we present a Bayesian linear regression model to predict the roll-to-roll plasma system’s electrode voltage and current; given a particular set of inputs. The inputs of the plasma system are power, pressure and concentration of gases; hydrogen, methane, oxygen, nitrogen and ...

Improving The Accuracy For The Long-Term Hydrologic Impact Assessment (L-Thia) Model, Anqi Zhang, Lawrence Theller, Bernard A. Engel

The Summer Undergraduate Research Fellowship (SURF) Symposium

Urbanization increases runoff by changing land use types from less impervious to impervious covers. Improving the accuracy of a runoff assessment model, the Long-Term Hydrologic Impact Assessment (L-THIA) Model, can help us to better evaluate the potential uses of Low Impact Development (LID) practices aimed at reducing runoff, as well as to identify appropriate runoff and water quality mitigation methods. Several versions of the model have been built over time, and inconsistencies have been introduced between the models. To improve the accuracy and consistency of the model, the equations and parameters (primarily curve numbers in the case of this model ...

Machine Learning In Xenon1t Analysis, Dillon A. Davis, Rafael F. Lang, Darryl P. Masson

The Summer Undergraduate Research Fellowship (SURF) Symposium

In process of analyzing large amounts of quantitative data, it can be quite time consuming and challenging to uncover populations of interest contained amongst the background data. Therefore, the ability to partially automate the process while gaining additional insight into the interdependencies of key parameters via machine learning seems quite appealing. As of now, the primary means of reviewing the data is by manually plotting data in different parameter spaces to recognize key features, which is slow and error prone. In this experiment, many well-known machine learning algorithms were applied to a dataset to attempt to semi-automatically identify known populations ...

Structure-Force Field Generator For Molecular Dynamics Simulations, Carlos M. Patiño, Lorena Alzate, Alejandro Strachan

The Summer Undergraduate Research Fellowship (SURF) Symposium

Atomistic and molecular simulations have become an important research field due to the progress made in computer performance and the necessity of new and improved materials. Despite this, first principle simulations of large molecules are still not possible because the high computational time and resources required. Other methods, such as molecular dynamics, allow the simplification of calculations by defining energy terms to describe multiple atom interactions without compromising accuracy significantly. A group of these energy terms is called a force field, and each force field has its own descriptions and parameters. The objective of this project was to develop a ...

Predicting Locations Of Pollution Sources Using Convolutional Neural Networks, Yiheng Chi, Nickolas D. Winovich, Guang Lin

The Summer Undergraduate Research Fellowship (SURF) Symposium

Pollution is a severe problem today, and the main challenge in water and air pollution controls and eliminations is detecting and locating pollution sources. This research project aims to predict the locations of pollution sources given diffusion information of pollution in the form of array or image data. These predictions are done using machine learning. The relations between time, location, and pollution concentration are first formulated as pollution diffusion equations, which are partial differential equations (PDEs), and then deep convolutional neural networks are built and trained to solve these PDEs. The convolutional neural networks consist of convolutional layers, reLU layers ...

Euler-Richardson Method Preconditioned By Weakly Stochastic Matrix Algebras: A Potential Contribution To Pagerank Computation, Stefano Cipolla, Carmine Di Fiore, Francesco Tudisco

Electronic Journal of Linear Algebra

Let S be a column stochastic matrix with at least one full row. Then S describes a Pagerank-like random walk since the computation of the Perron vector x of S can be tackled by solving a suitable M-matrix linear system Mx = y, where M = I − τ A, A is a column stochastic matrix and τ is a positive coefficient smaller than one. The Pagerank centrality index on graphs is a relevant example where these two formulations appear. Previous investigations have shown that the Euler- Richardson (ER) method can be considered in order to approach the Pagerank computation problem by means ...

Querying And Visualization Of Moving Objects Using Constraint Databases, Semere M. Woldemariam

Computer Science and Engineering: Theses, Dissertations, and Student Research

Good querying and visualization of moving objects and their trajectories is still an open problem. This thesis investigates three types of moving objects. First, projectiles, whose parabolic motion is difficult to represent. Second, moving objects that slide down a slope. The representation of these objects is challenging because of their accelerating motion. Third, the motion of migrating animals. The motion of migrating animals is challenging because it also involves some spatio-temporal interpolation. The thesis shows a solution to these problems using ideas from physics and an implementation in the MLPQ constraint databases system. The MLPQ implementation enables several complex spatio-temporal ...

Contextual Motivation In Physical Activity By Means Of Association Rule Mining, Sugam Sharma, Udoyara Sunday Tim, Marinelle Payton, Hari Cohly, Shashi Gadia, Johnny Wong, Sudharshanam Karakala

Sugam Sharma

The primary thrust of this work is to demonstrate the applicability of association rule mining in public health domain, focusing on physical activity and exercising. In this paper, the concept of association rule mining is shown assisting to promote the physical exercise as regular human activity. Specifically, similar to the prototypical example of association rule mining, market basket analysis, our proposed novel approach considers two events – exercise (sporadic) and sleep (regular) as the two items of the frequent set; and associating the former, exercise event, with latter, the daily occurring activity sleep at night, helps strengthening the frequency of the ...

Treatment Of Fast Chemistry In Fdf/Les: In Situ Adaptive Tabulation, Van Vliet E., Rodney O. Fox, J. J. Derksen, S. B. Pope

Rodney O. Fox

The feasibility to implement fast-chemistry reactions in a three-dimensional large eddy simulation (LES) of a turbulent reacting flow using a filtered density function (PDF) technique is studied. Low-density polyethylene (LDPE) is used as an representative reaction due to the stiff nature of the ordinary differential equation (ODE's) describing the kinetics. In FDF/LES, the chemistry needs to be evaluated many times for a large number of fictitious particles that are tracked in the flow, and therefore a constraint is put to the CPU time needed to solve the kinetics. Pope (1997) developed an in situ adaptive tabulation (ISAT) to ...

Contextual Motivation In Physical Activity By Means Of Association Rule Mining, Sugam Sharma, Udoyara Sunday Tim, Marinelle Payton, Hari Cohly, Shashi Gadia, Johnny Wong, Sudharshanam Karakala

Johnny Wong

The primary thrust of this work is to demonstrate the applicability of association rule mining in public health domain, focusing on physical activity and exercising. In this paper, the concept of association rule mining is shown assisting to promote the physical exercise as regular human activity. Specifically, similar to the prototypical example of association rule mining, market basket analysis, our proposed novel approach considers two events – exercise (sporadic) and sleep (regular) as the two items of the frequent set; and associating the former, exercise event, with latter, the daily occurring activity sleep at night, helps strengthening the frequency of the ...

Design Study Of A Heavy Duty Load Cell Using Finite Element Analysis: A Practical Introduction To Mechatronic Design Process, Mohamad Saleh

The ITB Journal

Mechatronics design process is a series of analytical brain storming operations from sepcification to implementation. The mechtronic products are widly available in the market for various use and applications. This is expected to develop futher in the future with great competitiveness. In order to succeed in the market, mechatronic products need to satisfy the market expectations with regard to quality, fitness for purpose, customer's appeal and cost efficiency. Therefore, the design analysis techniques play a significat part in the market success of these products. Finite Element Analysis is probably the most commonly used numerical technique for mechatronic product design ...

The Acquisition And Analysis Of Electroencephalogram Data For The Classification Of Benign Partial Epilepsy Of Childhood With Centrotemporal Spikes, Jessica A. Scarborough

Master's Theses

In this thesis, I will expand upon each step in the process of acquiring and analyzing electroencephalogram (EEG) for the classification of benign childhood epilepsy with centrotemporal spikes. Despite huge advancements in the field of health informatics—natural language processing, machine learning, predictive modeling—there are significant barriers to the access of clinical data. These barriers include information blocking, privacy policy concerns, and a lack of stakeholder support. We will see that these roadblocks are all responsible for stunting biomedical research in some way, including my own experiences in acquiring the data for the second chapter of this thesis.

This ...

Red Beetl: Recipe Encoder Decoder Beer Translator Lstm, Grace Ermi, Ellyn Ayton

Graduate Student Conference

The number of craft breweries has exploded in last decade: there are around a dozen breweries in Bellingham alone. Each brewery must assemble a lineup of beers, but this process of designing new beers usually relies on some combination of brewer instinct and trial and error. Because brewing beer involves complicated biological and chemical processes, the mapping from recipe to the beer it will produce is non-trivial to predict. In this project, we consider mapping between representations of beer in three distinct domains. In one view, a beer can be described by a recipe, which specifies the particular hop, malt ...

Mining Of Primary Healthcare Patient Data With Selective Multimorbid Diseases, Annette Megerdichian Azad

Electronic Thesis and Dissertation Repository

Despite a large volume of research on the prognosis, diagnosis and overall burden of multimorbidity, very little is known about socio-demographic characteristics of multimorbid patients. This thesis aims to analyze the socio-demographic characteristics of patients with multiple chronic conditions (multimorbidity), focusing on patient groups sharing the same combination of diseases. Several methods were explored to analyze the co-occurrence of multiple chronic diseases as well as the associations between socio-demographics and chronic conditions. These methods include disease pair distributions over gender, age groups and income level quintiles, Multimorbidity Coefficients for measuring the concurrence of disease pairs and triples, and k-modes clustering ...

Accuracy And Stability Of Integration Methods For Neutrino Transport In Core Collapse Supernovae, Kyle A. Gregory

University of Tennessee Honors Thesis Projects

No abstract provided.