Physical Sciences and Mathematics Commons^™

Ehugs: Enhanced Hierarchical Unbiased Graph Shrinkage For Efficient Groupwise Registration, Guorong Wu, Xuewei Peng, Shihui Ying, Qian Wang, Pew-Thian Yap, Dan Shen, Dinggang Shen

Mathematics and Statistics Faculty Publications

Effective and efficient spatial normalization of a large population of brain images is critical for many clinical and research studies, but it is technically very challenging. A commonly used approach is to choose a certain image as the template and then align all other images in the population to this template by applying pairwise registration. To avoid the potential bias induced by the inappropriate template selection, groupwise registration methods have been proposed to simultaneously register all images to a latent common space. However, current groupwise registration methods do not make full use of image distribution information for more accurate registration. …

Algorithms Leveraging Smartphone Sensing For Analyzing Explosion Events, Srinivas Chakravarthi Thandu

Doctoral Dissertations

"The increasing frequency of explosive disasters throughout the world in recent years have created a clear need for the systems to monitor for them continuously to improve the post-disaster emergency events such as rescue and recovery operations. Disasters both man-made and natural are unfortunate and not preferred, however monitoring them may be a lifesaving phenomenon in emergency scenarios. Dedicated sensors deployed in the public places and their associated networks to monitor such events may be inadequate and must be complemented for making the monitoring more pervasive and effective. In the recent past, modern smartphones with significant processing, networking and storage …

Efficient Execution Of Top-K Closeness Centrality Queries, Paul W. Olsen

Legacy Theses & Dissertations (2009 - 2024)

Many of today's applications can benefit from the discovery of the most central entities in real-world networks.

Evaluating And Improving The Efficiency Of Software And Algorithms For Sequence Data Analysis, Hugh L. Eaves

Theses and Dissertations

With the ever-growing size of sequence data sets, data processing and analysis are an increasingly large portion of the time and money spent on nucleic acid sequencing projects. Correspondingly, the performance of the software and algorithms used to perform that analysis has a direct effect on the time and expense involved. Although the analytical methods are widely varied, certain types of software and algorithms are applicable to a number of areas. Targeting improvements to these common elements has the potential for wide reaching rewards. This dissertation research consisted of several projects to characterize and improve upon the efficiency of several …

Applications Of Computational Geometry And Computer Vision, Joseph Lemley

All Master's Theses

Recent advances in machine learning research promise to bring us closer to the original goals of artificial intelligence. Spurred by recent innovations in low-cost, specialized hardware and incremental refinements in machine learning algorithms, machine learning is revolutionizing entire industries. Perhaps the biggest beneficiary of this progress has been the field of computer vision. Within the domains of computational geometry and computer vision are two problems: Finding large, interesting holes in high dimensional data, and locating and automatically classifying facial features from images. State of the art methods for facial feature classification are compared and new methods for finding empty hyper-rectangles …

Improving Patient Safety, Health Data Accuracy, And Remote Self-Management Of Health Through The Establishment Of A Biometric-Based Global Uhid, Guy Hembroff

Michigan Tech Publications, Part 1

Healthcare systems globally continue to face challenges surrounding patient identification. Consequences of misidentification include incomplete and inaccurate electronic patient health records potentially jeopardizing patients' safety, a significant amount of cases of medical fraud because of inadequate identification mechanisms, and difficulties affiliated with the value of remote health self-management application data being aggregated accurately into the user's Electronic Health Record (EHR). We introduce a new technique of user identification in healthcare capable of establishing a global identifier. Our research has developed algorithms capable of establishing a Unique Health Identifier (UHID) based on the user's fingerprint biometric, with the utilization of facial-recognition …

An Efficient Method For Optimizing Segmentation Parameters, Jacob D' Avy, Wei-Wen Hsu, Chung-Hao Chen, Andreas F. Koschan, Mongi Abidi

Electrical & Computer Engineering Faculty Publications

Segmenting an image into meaningful regions is an important step in many computer vision applications such as facial recognition, target tracking and medical image analysis. Because image segmentation is an ill-posed problem, parameters are needed to constrain the solution to one that is suitable for a given application. For a user, setting parameter values is often unintuitive. We present a method for automating segmentation parameter selection using an efficient search method to optimize a segmentation objective function. Efficiency is improved by utilizing prior knowledge about the relationship between a segmentation parameter and the objective function terms. An adaptive sampling of …

Filters And Matrix Factorization, Myung-Sin Song, Palle E. T. Jorgensen

SIUE Faculty Research, Scholarship, and Creative Activity

We give a number of explicit matrix-algorithms for analysis/synthesis

in multi-phase filtering; i.e., the operation on discrete-time signals which

allow a separation into frequency-band components, one for each of the

ranges of bands, say N , starting with low-pass, and then corresponding

filtering in the other band-ranges. If there are N bands, the individual

filters will be combined into a single matrix action; so a representation of

the combined operation on all N bands by an N x N matrix, where the

corresponding matrix-entries are periodic functions; or their extensions to

functions of a complex variable. Hence our setting entails …

On The Analysis Of Some Recursive Equations In Probability., Arunangshu Biswas Dr.

Doctoral Theses

This thesis deals with recursive systems used in theoretical and applied probability. Recursive systems are stochastic processes {Xn}n≥1 where the Xn depends on the earlier Xn−1 and also on some increment process which is uncorrelated with the process Xn. The simplest example of a recursive system is the Random Walk, whose properties have been extensively studied. Mathematically a recursive system takes the form Xn = f(Xn−1, n), is the increment/ innovation procedure and f(·, ·) is a function on the product space of xn and n. We first consider a recursive system called Self-Normalized sums (SNS) corresponding to a sequence …

Development And Validation Of An Epitope Prediction Tool For Swine (Pigmatrix) Based On The Pocket Profile Method, Andres H. Gutiérrez, William D. Martin, Chris Bailey-Kellogg, Frances Terry, Leonard Moise, Anee S. De Groot

Dartmouth Scholarship

Background: T cell epitope prediction tools and associated vaccine design algorithms have accelerated the development of vaccines for humans. Predictive tools for swine and other food animals are not as well developed, primarily because the data required to develop the tools are lacking. Here, we overcome a lack of T cell epitope data to construct swine epitope predictors by systematically leveraging available human information. Applying the “pocket profile method ”, we use sequence and structural similarities in the binding pockets of human and swine major histocompatibility complex proteins to infer Swine Leukocyte Antigen (SLA) peptide binding preferences. We developed epitope-prediction …

Answering Why-Not Questions On Reverse Top-K Queries, Yunjun Gao, Qing Liu, Gang Chen, Baihua Zheng, Linlin Zhou

Research Collection School Of Computing and Information Systems

Why-not questions, which aim to seek clarifications on the missing tuples for query results, have recently received considerable attention from the database community. In this paper, we systematically explore why-not questions on reverse top-k queries, owing to its importance in multi-criteria decision making. Given an initial reverse top-k query and a missing/why-not weighting vector set Wm that is absent from the query result, why-not questions on reverse top-k queries explain why Wm does not appear in the query result and provide suggestions on how to refine the initial query with minimum penalty to include Wm in the refined query result. …

Evaluation Of Data-Path Topologies For Self-Timed Conditional Statements, Navaneeth Prasannakumar Jamadagni

Dissertations and Theses

This research presents a methodology to evaluate data path topologies that implement a conditional statement for an average-case performance that is better than the worst-case performance. A conditional statement executes one of many alternatives depending on how Boolean conditions evaluate to true or false. Alternatives with simple computations take less time to execute. The self-timed designs can exploit the faster executing alternatives and provide an average-case behavior, where the average depends on the frequency of simple and complex computations, and the difference in the completion times of simple and complex computations. The frequency of simple and complex computations depends on …

The Pc-Tree Algorithm, Kuratowski Subdivisions, And The Torus., Charles J. Suer

Electronic Theses and Dissertations

The PC-Tree algorithm of Shih and Hsu (1999) is a practical linear-time planarity algorithm that provides a plane embedding of the given graph if it is planar and a Kuratowski subdivision otherwise. Remarkably, there is no known linear-time algorithm for embedding graphs on the torus. We extend the PC-Tree algorithm to a practical, linear-time toroidality test for K3;3-free graphs called the PCK-Tree algorithm. We also prove that it is NP-complete to decide whether the edges of a graph can be covered with two Kuratowski subdivisions. This greatly reduces the possibility of a polynomial-time toroidality testing algorithm based solely on edge-coverings …

On Supervised And Unsupervised Methodologies For Mining Of Text Data., Tanmay Basu Dr.

Doctoral Theses

The supervised and unsupervised methodologies of text mining using the plain text data of English language have been discussed. Some new supervised and unsupervised methodologies have been developed for effective mining of the text data after successfully overcoming some limitations of the existing techniques.The problems of unsupervised techniques of text mining, i.e., document clustering methods are addressed. A new similarity measure between documents has been designed to improve the accuracy of measuring the content similarity between documents. Further, a hierarchical document clustering technique is designed using this similarity measure. The main significance of the clustering algorithm is that the number …

Hyperlegality And Heightened Surveillance: The Case Of Threatened Species Lists, Irus Braverman

Journal Articles

My contribution to the Debate "Thinking about Law and Surveillance" focuses on the project of governing nonhuman species through care, briefly pointing to how law and surveillance are interwoven in this context and to how conservation's biopolitical regimes are increasingly becoming more abstract, standardized, calculable, and algorithmic in scope. I argue that conservation’s focus on governing through care lends itself to heightened modes of surveillance and to hyperlegality - namely, to the intensified inspection and regulation of both governed and governing actors. I start with some preliminary explanations about my atypical use of the terms surveillance, law, and biopolitics.

Probabilistic Inference Based Message-Passing For Resource Constrained Dcops, Supriyo Ghosh, Akshat Kumar, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Distributed constraint optimization (DCOP) is an important framework for coordinated multiagent decision making. We address a practically useful variant of DCOP, called resource-constrained DCOP (RC-DCOP), which takes into account agents’ consumption of shared limited resources. We present a promising new class of algorithm for RC-DCOPs by translating the underlying co- ordination problem to probabilistic inference. Using inference techniques such as expectation- maximization and convex optimization machinery, we develop a novel convergent message-passing algorithm for RC-DCOPs. Experiments on standard benchmarks show that our approach provides better quality than previous best DCOP algorithms and has much lower failure rate. Comparisons against an …

Stochastic Optimization Via Forward Slice, Bob A. Salim, Lurdes Y. T. Inoue

UW Biostatistics Working Paper Series

Optimization consists of maximizing or minimizing a real-valued objective function. In many problems, the objective function may not yield closed-form solutions. Over many decades, optimization methods, both deterministic and stochastic, have been developed to provide solutions to these problems. However, some common limitations of these methods are the sensitivity to the initial value and that often current methods only find a local (non-global) extremum. In this article, we propose an alternative stochastic optimization method, which we call "Forward Slice", and assess its performance relative to available optimization methods.

Trip: Tracking Rhythms In Plants, An Automated Leaf Movement Analysis Program For Circadian Period Estimation, Kathleen Greenham, Ping Lou, Sara E. Remsen, Hany Farid, C Robertson Mcclung

Dartmouth Scholarship

Background: A well characterized output of the circadian clock in plants is the daily rhythmic movement of leaves. This process has been used extensively in Arabidopsis to estimate circadian period in natural accessions as well as mutants with known defects in circadian clock function. Current methods for estimating circadian period by leaf movement involve manual steps throughout the analysis and are often limited to analyzing one leaf or cotyledon at a time.

Methods: In this study, we describe the development of TRiP (Tracking Rhythms in Plants), a new method for estimating circadian period using a motion estimation algorithm that can …

Optimal "Big Data" Aggregation Systems - From Theory To Practical Application, William J. Culhane Iv

Open Access Dissertations

The integration of computers into many facets of our lives has made the collection and storage of staggering amounts of data feasible. However, the data on its own is not so useful to us as the analysis and manipulation which allows manageable descriptive information to be extracted. New tools to extract this information from ever growing repositories of data are required.

Some of these analyses can take the form of a two phase problem which is easily distributed to take advantage of available computing power. The first phase involves computing some descriptive partial result from some subset of the original …

Optcluster : An R Package For Determining The Optimal Clustering Algorithm And Optimal Number Of Clusters., Michael N. Sekula

Electronic Theses and Dissertations

Determining the best clustering algorithm and ideal number of clusters for a particular dataset is a fundamental difficulty in unsupervised clustering analysis. In biological research, data generated from Next Generation Sequencing technology and microarray gene expression data are becoming more and more common, so new tools and resources are needed to group such high dimensional data using clustering analysis. Different clustering algorithms can group data very differently. Therefore, there is a need to determine the best groupings in a given dataset using the most suitable clustering algorithm for that data. This paper presents the R package optCluster as an efficient …

Sensitivity Of Mixed Models To Computational Algorithms Of Time Series Data, Gunaime Nevine

Doctoral Dissertations

Statistical analysis is influenced by implementation of the algorithms used to execute the computations associated with various statistical techniques. Over many years; very important criteria for model comparison has been studied and examined, and two algorithms on a single dataset have been performed numerous times. The goal of this research is not comparing two or more models on one dataset, but comparing models with numerical algorithms that have been used to solve them on the same dataset.

In this research, different models have been broadly applied in modeling and their contrasting which are affected by the numerical algorithms in different …

Spectral Gene Set Enrichment (Sgse), H Robert Frost, Zhigang Li, Jason H. Moore

Dartmouth Scholarship

Gene set testing is typically performed in a supervised context to quantify the association between groups of genes and a clinical phenotype. In many cases, however, a gene set-based interpretation of genomic data is desired in the absence of a phenotype variable. Although methods exist for unsupervised gene set testing, they predominantly compute enrichment relative to clusters of the genomic variables with performance strongly dependent on the clustering algorithm and number of clusters. We propose a novel method, spectral gene set enrichment (SGSE), for unsupervised competitive testing of the association between gene sets and empirical data sources. SGSE first computes …

Generic Constructions Of Different Cryptographic Primitives Over Various Public Key Paradigms., Sumit Kumar Pandey Dr.

Doctoral Theses

In this thesis, we study the generic construction of some cryptographic primitives over various public key paradigms like traditional Public Key Cryptosystems and Identity Based Cryptosystems. It can be broadly divided into two categories1. Generic construction of some highly secure cryptographic primitives from less secure cryptographic primitives, and2. Generic construction of some complex cryptographic primitives from basic cryptographic primitives. Mathematical tools provide a way to achieve cryptographic functionality like confidentiality, authentication, data-integrity, non-repudiation etc., but in the case of complex cryptographic functionality like achieving confidentiality and authentication at the same time or confidentiality, authentication and non-repudiation at the same time …

Extracting City Traffic Events From Social Streams, Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, Amit P. Sheth

Kno.e.sis Publications

Cities are composed of complex systems with physical, cyber, and social components. Current works on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present …

A Dynamic Programming Algorithm For Finding The Optimal Placement Of A Secondary Structure Topology In Cryo-Em Data, Abhishek Biswas, Desh Ranjan, Mohammad Zubair, Jing He

Computer Science Faculty Publications

The determination of secondary structure topology is a critical step in deriving the atomic structures from the protein density maps obtained from electron cryomicroscopy technique. This step often relies on matching the secondary structure traces detected from the protein density map to the secondary structure sequence segments predicted from the amino acid sequence. Due to inaccuracies in both sources of information, a pool of possible secondary structure positions needs to be sampled. One way to approach the problem is to first derive a small number of possible topologies using existing matching algorithms, and then find the optimal placement for each …

Modeling User Transportation Patterns Using Mobile Devices, Erfan Davami

Electronic Theses and Dissertations

Participatory sensing frameworks use humans and their computing devices as a large mobile sensing network. Dramatic accessibility and affordability have turned mobile devices (smartphone and tablet computers) into the most popular computational machines in the world, exceeding laptops. By the end of 2013, more than 1.5 billion people on earth will have a smartphone. Increased coverage and higher speeds of cellular networks have given these devices the power to constantly stream large amounts of data. Most mobile devices are equipped with advanced sensors such as GPS, cameras, and microphones. This expansion of smartphone numbers and power has created a sensing …

Spectral Decomposition Of The Scattered Light Due To Deposits On The Solar Panel Surface, And Cross Correlated To Power Loss, Suzanna Ho

UNLV Theses, Dissertations, Professional Papers, and Capstones

The electric energy generated by solar panels declines due to dust particulates, bird deposits, water spots, and other contaminants that inhibit sunlight absorption and promote light scattering. As part of our research, we use cameras to capture images of solar panels, and analyze the images to detect the amount of scattered light. The more scattered light there is, the less light there is to penetrate the solar panel glass and reach the part of the panel that converts incident light to electric energy; therefore, less energy is generated. In this paper, we discuss the classification algorithm we developed to classify …

Nonsmooth Algorithms And Nesterov's Smoothing Technique For Generalized Fermat-Torricelli Problems, Nguyen Mau Nam, Nguyen Thai An, R. Blake Rector, Jie Sun

Mathematics and Statistics Faculty Publications and Presentations

We present algorithms for solving a number of new models of facility location which generalize the classical Fermat--Torricelli problem. Our first approach involves using Nesterov's smoothing technique and the minimization majorization principle to build smooth approximations that are convenient for applying smooth optimization schemes. Another approach uses subgradient-type algorithms to cope directly with the nondifferentiability of the cost functions. Convergence results of the algorithms are proved and numerical tests are presented to show the effectiveness of the proposed algorithms.

Applications Of The Wei-Lachin Multivariate One-Sided Test For Multiple Outcomes On Possibly Different Scales, John M. Lachin

GW Biostatistics Center

Many studies aim to assess whether a therapy has a beneficial effect on multiple outcomes simultaneously relative to a control. Often the joint null hypothesis of no difference for the set of outcomes is tested using separate tests with a correction for multiple tests, or using a multivariate T2-like MANOVA or global test. However, a more powerful test in this case is a multivariate one-sided or one-directional test directed at detecting a simultaneous beneficial treatment effect on each outcome, though not necessarily of the same magnitude. The Wei-Lachin test is a simple 1 df test obtained from a simple …

The Computational Complexity Of Some Games And Puzzles With Theoretical Applications, Vasiliki Despoina Mitsou

Dissertations, Theses, and Capstone Projects

The subject of this thesis is the algorithmic properties of one- and two-player

games people enjoy playing, such as Sudoku or Chess. Questions asked about puzzles

and games in this context are of the following type: can we design efficient computer

programs that play optimally given any opponent (for a two-player game), or solve

any instance of the puzzle in question?

We examine four games and puzzles and show algorithmic as well as intractability

results. First, we study the wolf-goat-cabbage puzzle, where a man wants to transport

a wolf, a goat, and a cabbage across a river by using a …