Computer Sciences Commons^™

Discovery And Characterization Of Circular And Chimeric Rnas In Complex Diseases, Xiaoxin Liu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Non-co-linear RNAs, including circular and chimeric RNAs, have been investigated in a broad range of cellular processes and various diseases. The current biogenesis models of circular and chimeric RNAs depend on RNA splicing mechanisms. We hypothesized that some of the non-co-linear RNAs are products of an alternative mechanism other than RNA splicing. To test this hypothesis, we developed a method, which is independent of genome annotations and splicing signals, to identify Circular and Chimeric RNAs of All Types (C2AT or CAT for simplicity) by analyzing the RNA species profiled by whole genome RNA sequencing. Statistical analysis and experimental validation revealed …

Mapping Transcription Factor Networks And Elucidating Their Biological Determinants, Yiming Kang

McKelvey School of Engineering Graduate Student Theses & Dissertations

A central goal in systems biology is to accurately map the transcription factor (TF) network of a cell. Such a network map is a key component for many downstream applications, from developmental biology to transcriptome engineering, and from disease modeling to drug discovery. Building a reliable network map requires a wide range of data sources including TF binding locations and gene expression data after direct TF perturbations. However, we are facing two roadblocks. First, rich resources are available only for a few well-studied systems and cannot be easily replicated for new organisms or cell types. Second, when TF binding and …

Holistic Control For Cyber-Physical Systems, Yehan Ma

McKelvey School of Engineering Graduate Student Theses & Dissertations

The Industrial Internet of Things (IIoT) are transforming industries through emerging technologies such as wireless networks, edge computing, and machine learning. However, IIoT technologies are not ready for control systems for industrial automation that demands control performance of physical processes, resiliency to both cyber and physical disturbances, and energy efficiency. To meet the challenges of IIoT-driven control, we propose holistic control as a cyber-physical system (CPS) approach to next-generation industrial automation systems. In contrast to traditional industrial automation systems where computing, communication, and control are managed in isolation, holistic control orchestrates the management of cyber platforms (networks and computing platforms) …

Convex Relaxations For Particle-Gradient Flow With Applications In Super-Resolution Single-Molecule Localization Microscopy, Hesam Mazidisharfabadi

McKelvey School of Engineering Graduate Student Theses & Dissertations

Single-molecule localization microscopy (SMLM) techniques have become advanced bioanalytical tools by quantifying the positions and orientations of molecules in space and time at the nanoscale. With the noisy and heterogeneous nature of SMLM datasets in mind, we discuss leveraging particle-gradient flow 1) for quantifying the accuracy of localization algorithms with and without ground truth and 2) as a basis for novel, model-driven localization algorithms with empirically robust performance. Using experimental data, we demonstrate that overlapping images of molecules, a typical consequence of densely packed biological structures, cause biases in position estimates and reconstruction artifacts. To minimize such biases, we develop …

Domain Specific Computing In Tightly-Coupled Heterogeneous Systems, Anthony Michael Cabrera

McKelvey School of Engineering Graduate Student Theses & Dissertations

Over the past several decades, researchers and programmers across many disciplines have relied on Moores law and Dennard scaling for increases in compute capability in modern processors. However, recent data suggest that the number of transistors per square inch on integrated circuits is losing pace with Moores laws projection due to the breakdown of Dennard scaling at smaller semiconductor process nodes. This has signaled the beginning of a new “golden age in computer architecture” in which the paradigm will be shifted from improving traditional processor performance for general tasks to architecting hardware that executes a class of applications in a …

Investigating Single Precision Floating General Matrix Multiply In Heterogeneous Hardware, Steven Harris

McKelvey School of Engineering Graduate Student Theses & Dissertations

The fundamental operation of matrix multiplication is ubiquitous across a myriad of disciplines. Yet, the identification of new optimizations for matrix multiplication remains relevant for emerging hardware architectures and heterogeneous systems. Frameworks such as OpenCL enable computation orchestration on existing systems, and its availability using the Intel High Level Synthesis compiler allows users to architect new designs for reconfigurable hardware using C/C++. Using the HARPv2 as a vehicle for exploration, we investigate the utility of several of the most notable matrix multiplication optimizations to better understand the performance portability of OpenCL and the implications for such optimizations on this and …

Csp-Completeness And Its Applications, Alexander Durgin

McKelvey School of Engineering Graduate Student Theses & Dissertations

We build off of previous ideas used to study both reductions between CSPrefutation problems and improper learning and between CSP-refutation problems themselves to expand some hardness results that depend on the assumption that refuting random CSP instances are hard for certain choices of predicates (like k-SAT). First, we are able argue the hardness of the fundamental problem of learning conjunctions in a one-sided PAC-esque learning model that has appeared in several forms over the years. In this model we focus on producing a hypothesis that foremost guarantees a small false-positive rate while minimizing the false-negative rate for such hypotheses. Further, …

Exploring Attacks And Defenses In Additive Manufacturing Processes: Implications In Cyber-Physical Security, Nicholas Deily

McKelvey School of Engineering Graduate Student Theses & Dissertations

Many industries are rapidly adopting additive manufacturing (AM) because of the added versatility this technology offers over traditional manufacturing techniques. But with AM, there comes a unique set of security challenges that must be addressed. In particular, the issue of part verification is critically important given the growing reliance of safety-critical systems on 3D printed parts. In this thesis, the current state of part verification technologies will be examined in the con- text of AM-specific geometric-modification attacks, and an automated tool for 3D printed part verification will be presented. This work will cover: 1) the impacts of malicious attacks on …

Exploring Usage Of Web Resources Through A Model Of Api Learning, Finn Voichick

McKelvey School of Engineering Graduate Student Theses & Dissertations

Application programming interfaces (APIs) are essential to modern software development, and new APIs are frequently being produced. Consequently, software developers must regularly learn new APIs, which they typically do on the job from online resources rather than in a formal educational context. The Kelleher–Ichinco COIL model, an acronym for “Collection and Organization of Information for Learning,” was recently developed to model the entire API learning process, drawing from information foraging theory, cognitive load theory, and external memory research. We ran an exploratory empirical user study in which participants performed a programming task using the React API with the goal of …

Real-Time Communication In Cloud Environments, Chong Li

McKelvey School of Engineering Graduate Student Theses & Dissertations

Real-time communication is critical to emerging cloud applications from smart cities to industrial automation. The new class of latency-critical applications requires latency differentiation and performance isolation in a highly scalable fashion in a virtualized cloud environments. This dissertation aims to develop novel cloud architecture and services to support real-time communication at both the platform and infrastructure layers. At the platform layer, we build SRTM, a scalable and real-time messaging middleware (platform) that features (1) latency differentiation, (2) service isolation through rate limiting, and (3) scalability through load distribution among messaging brokers. A key contribution of SRTM lies in the exploitation …

Toward Efficient Scheduling For Parallel Real-Time Tasks On Multiprocessors, Son Ngoc Dinh

McKelvey School of Engineering Graduate Student Theses & Dissertations

Modern real-time applications are becoming more demanding computationally while their temporal requirements, dictated by the physical world, often remain unchanged. This coupled with the increasing prevalence of multiprocessors in real-time systems necessitates that highly computation-demanding real-time tasks need to be parallelized to exploit the parallelism offered by the underlying hardware, in order to satisfy their temporal constraints. Scheduling parallel real-time tasks, however, introduces a new layer of complexity due to the allowance for intra-task parallelism. This dissertation addresses the problem of scheduling parallel real-time tasks in which tasks may (or may not) access shared non-processor resources, such as in-memory buffers …

Elicitation And Aggregation Of Data In Knowledge Intensive Crowdsourcing, Dohoon Kim

All Computer Science and Engineering Research

With the significant advance of internet and connectivity, crowdsourcing gained more popularity and various crowdsourcing platforms emerged. This project focuses on knowledge-intensive crowdsourcing, in which agents are presented with the tasks that require certain knowledge in domain. Knowledge-intensive crowdsourcing requires agents to have experiences on the specific domain. With the constraint of resources and its trait as sourcing from crowd, platform is likely to draw agents with different levels of expertise and knowledge and asking same task can result in bad performance. Some agents can give better information when they are asked with more general question or more knowledge-specific task …

A Virtual 4d Ct Scanner, Xiwen Li

All Computer Science and Engineering Research

4D CT scan is widely used in medical imaging. Images are acquired through phases. In this case, we can track the motion of organs such as heart. However, it also introduces motion artifacts. A lot of research focuses on remove these artifacts. It is difficult to acquire artifact data by a real CT scanner. In this project, we implement a virtual CT machine to simulate the real 4D CT scan. we also conduct experi- ments to check its clinical reality with respect to respiratory and heart motion parameters.

Centrality Of Blockchain, Zixuan Li

All Computer Science and Engineering Research

Decentralization is widely recognized as the property and one of most important advantage of blockchain over legacy systems. However, decentralization is often discussed on the consensus layer and recent research shows the trend of centralization on several subsystem of blockchain. In this project, we measured centralization of Bitcoin and Ethereum on source code, development eco-system, and network node levels. We found that the programming language of project is highly centralized, code clone is very common inside Bitcoin and Ethereum community, and developer contribution distribution is highly centralized. We further discuss how could these centralizations lead to security issues in blockchain. …

Solving Disappearance At Gastech With Visual Analytic Techniques, Saulet Yskak

All Computer Science and Engineering Research

We are living in a society, where images and charts speak louder than words. Therefore, information visualization plays a major role in solving complex problems since it provides a visual summary of data that makes it easier to identify trends and patterns.

In this master project, I propose a web – based visual analytics tool that enables to analyze complex email and time based / event series data. The visual analytics framework uses test data from IEEE VAST Challenge 2014: Mini challenge 1 that concentrated on the disappearance of employees of a fictional GAStech company, but the tool allows users …

Predicting Disease Progression Using Deep Recurrent Neural Networks And Longitudinal Electronic Health Record Data, Seunghwan Kim

McKelvey School of Engineering Graduate Student Theses & Dissertations

Electronic Health Records (EHR) are widely adopted and used throughout healthcare systems and are able to collect and store longitudinal information data that can be used to describe patient phenotypes. From the underlying data structures used in the EHR, discrete data can be extracted and analyzed to improve patient care and outcomes via tasks such as risk stratification and prospective disease management. Temporality in EHR is innately present given the nature of these data, however, and traditional classification models are limited in this context by the cross-sectional nature of training and prediction processes. Finding temporal patterns in EHR is especially …

Predicting Disease Progression Using Deep Recurrent Neural Networks And Longitudinal Electronic Health Record Data, Seunghwan Kim

McKelvey School of Engineering Graduate Student Theses & Dissertations

Electronic Health Records (EHR) are widely adopted and used throughout healthcare systems and are able to collect and store longitudinal information data that can be used to describe patient phenotypes. From the underlying data structures used in the EHR, discrete data can be extracted and analyzed to improve patient care and outcomes via tasks such as risk stratification and prospective disease management. Temporality in EHR is innately present given the nature of these data, however, and traditional classification models are limited in this context by the cross- sectional nature of training and prediction processes. Finding temporal patterns in EHR is …

The Effects Of Mixed-Initiative Visualization Systems On Exploratory Data Analysis, Adam Kern

McKelvey School of Engineering Graduate Student Theses & Dissertations

The main purpose of information visualization is to act as a window between a user and data. Historically, this has been accomplished via a single-agent framework: the only decisionmaker in the relationship between visualization system and analyst is the analyst herself. Yet this framework arose not from first principles, but from necessity: prior to this decade, computers were limited in their decision-making capabilities, especially in the face of large, complex datasets and visualization systems. This thesis aims to present the design and evaluation of a mixed-initiative system that aids the user in handling large, complex datasets and dense visualization systems. …

The Effects Of Mixed-Initiative Visualization Systems On Exploratory Data Analysis, Alvitta Ottley, Adam Kern

All Computer Science and Engineering Research

The primary purpose of information visualization is to act as a window between a user and the data. Historically, this has been accomplished via a single-agent framework: the only decision-maker in the relationship between visualization system and analyst is the analyst herself. Yet this framework arose not from first principles, but a necessity. Before this decade, computers were limited in their decision-making capabilities, especially in the face of large, complex datasets and visualization systems. This paper aims to present the design and evaluation of a mixed-initiative system that aids the user in handling large, complex datasets and dense visualization systems. …

Graph Deep Learning: Methods And Applications, Muhan Zhang

McKelvey School of Engineering Graduate Student Theses & Dissertations

The past few years have seen the growing prevalence of deep neural networks on various application domains including image processing, computer vision, speech recognition, machine translation, self-driving cars, game playing, social networks, bioinformatics, and healthcare etc. Due to the broad applications and strong performance, deep learning, a subfield of machine learning and artificial intelligence, is changing everyone's life.Graph learning has been another hot field among the machine learning and data mining communities, which learns knowledge from graph-structured data. Examples of graph learning range from social network analysis such as community detection and link prediction, to relational machine learning such as …

Towards Interpretable Machine Learning With Applications To Clinical Decision Support, Zhicheng Cui

McKelvey School of Engineering Graduate Student Theses & Dissertations

Machine learning models have achieved impressive predictive performance in various applications such as image classification and object recognition. However, understanding how machine learning models make decisions is essential when deploying those models in critical areas such as clinical prediction and market analysis, where prediction accuracy is not the only concern. For example, in the clinical prediction of ICU transfers, in addition to accurate predictions, doctors need to know the contributing factors that triggered the alert, which factors can be quickly altered to prevent the ICU transfer. While interpretable machine learning has been extensively studied for years, challenges remain as among …

Point Cloud Processing With Neural Networks, Stephanie Miller, Jiahao Li

All Computer Science and Engineering Research

In this project, we explore new techniques and architectures for applying deep neural networks when the input is point cloud data. We first consider applying convolutions on regular pixel and voxel grids, using polynomials of point coordinates and Fourier transforms to get a rich feature representation for all points mapped to the same pixel or voxel. We also apply these ideas to generalize the recently proposed "interpolated convolution", by learning continuous-space kernels as a combination of polynomial and Fourier basis kernels. Experiments on the ModelNet40 dataset demonstrate that our methods have superior performance over the baselines in 3D object recognition.

Static Taint Analysis Of Binary Executables Using Architecture-Neutral Intermediate Representation, Elaine Cole

All Computer Science and Engineering Research

Ghidra, National Security Agency’s powerful reverse engineering framework, was recently released open-source in April 2019 and is capable of lifting instructions from a wide variety of processor architectures into its own register transfer language called p-code. In this project, we present a new tool which leverages Ghidra’s specific architecture-neutral intermediate representation to construct a control flow graph modeling all program executions of a given binary and apply static taint analysis. This technique is capable of identifying the information flow of malicious input from untrusted sources that may interact with key sinks or parts of the system without needing access to …

Shared Or Dedicated Infrastructures: On The Impact Of Reprovisioning Ability, Roch A. Guérin, Kartik Hosanagar, Xinxin Li, Soumya Sen

Computer Science and Engineering Faculty Research

New technologies, such as virtualization, are transforming the way in which software and services are deployed and delivered to their users. They are behind the emergence of IT offerings such as cloud computing and converged networks, and manifest themselves through two important trends: (1) lower the cost of sharing a common infrastructure across multiple services with disparate resource requirements, and (2) dynamic provi- sioning of capacity in response to demand. Conventional wisdom is that both of these capabilities are synergistic, with greater provisioning flexibility improving the benefits derived from sharing computing or network resources. Consequently, a service operator should now …

Event Reconstruction In The Advanced Particle-Astrophysics Telescope, Emily Ramey

McKelvey School of Engineering Graduate Student Theses & Dissertations

The Advanced Particle-Astrophysics Telescope (APT) is a concept for a gamma-ray space telescope operating in the keV to MeV energy range. Due to the nature of the telescope and the physics of detection, reconstructing initial photon trajectories can be very computationally complex. This is a barrier to the real-time detection of astrophysical transient phenomena such as Gamma Ray Bursts (GRBs), and a faster reconstruction algorithm is needed in order to effectively study them. In this project, we develop such an algorithm based on Boggs & Jean (2000) and discuss the effects of certain algorithmic parameters on computational performance. For testing, …

Pipelined Parallelism In A Work-Stealing Scheduler, Thomas Kelly

All Computer Science and Engineering Research

A pipeline is a particular type of parallel program structure, often used to represent loops with cross-iteration dependencies. Pipelines cannot be expressed with the typical parallel language constructs offered by most environments. Therefore, in order to run pipelines, it is necessary to write a parallel language and scheduler with specialized support for them. Some such schedulers are written exclusively for pipelines and unable to run any other type of program, which allows for certain optimizations that take advantage of the pipeline structure. Other schedulers implement support for pipelines on top of a general-purpose scheduling algorithm. One example of such an …

Decoupling Information And Connectivity Via Information-Centric Transport, Hila Ben Abraham

McKelvey School of Engineering Graduate Student Theses & Dissertations

The power of Information-Centric Networking architectures (ICNs) lies in their abstraction for communication --- the request for named data. This abstraction was popularized by the HyperText Transfer Protocol (HTTP) as an application-layer abstraction, and was extended by ICNs to also serve as their network-layer abstraction. In recent years, network mechanisms for ICNs, such as scalable name-based forwarding, named-data routing and in-network caching, have been widely explored and researched. However, to the best of our knowledge, the impact of this network abstraction on ICN applications has not been explored or well understood. The motivation of this dissertation is to address this …

Period And Computational Elasticity For Adaptive Real-Time Systems, James Wiliam Orr

McKelvey School of Engineering Graduate Student Theses & Dissertations

A wide range range of real-world applications (including multimedia players, ad-hoc communication networks, online trading, radar tracking software, and other adaptive control algorithms) need adaptive adjustment to their resource utilizations at run-time, while still maintaining real-time guarantees. The elastic task model of soft real-time systems allows for the run-time manipulation of tasks’ processor utilizations in order to maintain a system-wide quality of service or accommodate needs of other tasks by assigning each task a period within a specified range. As originally presented, only sequential tasks executing on a single processor were considered. However, in the two decades since the elastic …

Scheduling Multiple Parallel Jobs Online, Kefu Lu

McKelvey School of Engineering Graduate Student Theses & Dissertations

The prevalence of parallel processing has only increased in recent years. Today, most computing machines available on the market shifted from using single processors to possessing a multicore architecture. Naturally, there has been considerable work in developing parallel programming languages and frameworks which programmers can use to leverage the computing power of these machines. These languages allow users to create programs with internal parallelism. The next, and crucial, step is to ensure that the computing system can efficiently execute these parallel jobs. Executing a single parallel job efficiently is a very well-studied problem in parallel computing. In the area of …

Automating Active Learning For Gaussian Processes, Gustavo Malkomes

McKelvey School of Engineering Graduate Student Theses & Dissertations

In many problems in science, technology, and engineering, unlabeled data is abundant but acquiring labeled observations is expensive -- it requires a human annotator, a costly laboratory experiment, or a time-consuming computer simulation. Active learning is a machine learning paradigm designed to minimize the cost of obtaining labeled data by carefully selecting which new data should be gathered next. However, excessive machine learning expertise is often required to effectively apply these techniques in their current form. In this dissertation, we propose solutions that further automate active learning. Our core contributions are active learning algorithms that are easy for non-experts to …