Physical Sciences and Mathematics Commons^™

Isquest: Finding Insertion Sequences In Prokaryotic Sequence Fragment Data, Abhishek Biswas, David T. Gauthier, Desh Ranjan, Mohammad Zubair

Computer Science Faculty Publications

Motivation: Insertion sequences (ISs) are transposable elements present in most bacterial and archaeal genomes that play an important role in genomic evolution. The increasing availability of sequenced prokaryotic genomes offers the opportunity to study ISs comprehensively, but development of efficient and accurate tools is required for discovery and annotation. Additionally, prokaryotic genomes are frequently deposited as incomplete, or draft stage because of the substantial cost and effort required to finish genome assembly projects. Development of methods to identify IS directly from raw sequence reads or draft genomes are therefore desirable. Software tools such as Optimized Annotation System for Insertion Sequences …

Estimating Cost Adjustments Required To Accomplish Target Savings In Chronic Disease Management Interventions: A Simulation Study, Rafael Diaz, Joshua G. Behr, Bruce S. Britton

VMASC Publications

Chronic diseases are persistent ailments that are not preventable or curable with medication or vaccination. Many of the leading chronic conditions in industrialized societies may be related to lifestyle choices. The prevalence of these chronic conditions significantly affects the health, suffering, and longevity of patients. This paper demonstrates the utility of system dynamics as an approach to model and simulate the behavior of key cost factors in the implementation of population health management interventions. The study uses modeling and simulation as an evaluative method to identify potential savings stemming from an intervention within a well-defined population group. The model is …

Changing Cpu Frequency In Comd Proxy Application Offloaded To Intel Xeon Phi Co-Processors, Gary Lawson, Masha Sosonkina, Yuzhong Shen

Computational Modeling & Simulation Engineering Faculty Publications

Obtaining exascale performance is a challenge. Although the technology of today features hardware with very high levels of concurrency, exascale performance is primarily limited by energy consumption. This limitation has lead to the use of GPUs and specialized hardware such as many integrated core (MIC) co-processors and FPGAs for computation acceleration. The Intel Xeon Phi co-processor, built upon the MIC architecture, features many low frequency, energy efficient cores. Applications, even those which do not saturate the large vector processing unit in each core, may benefit from the energy-efficient hardware and software of the Xeon Phi. This work explores the energy …

Potential Of Cognitive Computing And Cognitive Systems, Ahmed K. Noor

Computational Modeling & Simulation Engineering Faculty Publications

Cognitive computing and cognitive technologies are game changers for future engineering systems, as well as for engineering practice and training. They are major drivers for knowledge automation work, and the creation of cognitive products with higher levels of intelligence than current smart products. This paper gives a brief review of cognitive computing and some of the cognitive engineering systems activities. The potential of cognitive technologies is outlined, along with a brief description of future cognitive environments, incorporating cognitive assistants - specialized proactive intelligent software agents designed to follow and interact with humans and other cognitive assistants across the environments. The …

Extreme-Scale Parallel Mesh Generation: Telescopic Approach, Nikos Chrisochoides, Andrey Chernikov, Daming Feng, Christos Tsolakis

Computer Science Faculty Publications

In this poster we focus and present our preliminary results pertinent to the integration of multiple parallel Delaunay mesh generation methods into a coherent hierarchical framework. The goal of this project is to study our telescopic approach and to develop Delaunay-based methods to explore concurrency at all hardware layers using abstractions at (a) medium-grain level for many cores within a single chip and (b) coarse-grain level, i.e., sub-domain level using proper error metric- and application-specific continuous decomposition methods.

Scaffolding To Improve Writing Skills In A Computer Science Literacy Course, Wu He, Harris Wu, Li Xu, Kurt Maly

Computer Science Faculty Publications

Writing has been recognized as an important skill in the technology field. This paper reports a study that uses a scaffolding approach to improve student writing skills in a computer science literacy course. While the quantitative results do not show a significant impact of scaffolding in individual paper assignment on the subsequent group wiki assignment, the student feedback in end-of-semester evaluations strongly indicated that scaffolding indeed helped improve their writing.

Reminiscing About 15 Years Of Interoperability Efforts, Herbert Van De Sompel, Michael L. Nelson

Computer Science Faculty Publications

Over the past fifteen years, our perspective on tackling information interoperability problems for web-based scholarship has evolved significantly. In this opinion piece, we look back at three efforts that we have been involved in that aptly illustrate this evolution: OAI-PMH, OAI-ORE, and Memento. Understanding that no interoperability specification is neutral, we attempt to characterize the perspectives and technical toolkits that provided the basis for these endeavors. With that regard, we consider repository-centric and web-centric interoperability perspectives, and the use of a Linked Data or a REST/HATEAOS technology stack, respectively. We also lament the lack of interoperability across nodes that play …

Making Sense Of Video Analytics: Lessons Learned From Clickstream Interactions, Attitudes, And Learning Outcome In A Video-Assisted Course, Michail N. Giannakos, Konstantinos Chorianopoulos, Nikos Chrisochoides

Computer Science Faculty Publications

Online video lectures have been considered an instructional media for various pedagogic approaches, such as the flipped classroom and open online courses. In comparison to other instructional media, online video affords the opportunity for recording student clickstream patterns within a video lecture. Video analytics within lecture videos may provide insights into student learning performance and inform the improvement of video-assisted teaching tactics. Nevertheless, video analytics are not accessible to learning stakeholders, such as researchers and educators, mainly because online video platforms do not broadly share the interactions of the users with their systems. For this purpose, we have designed an …

Scalable 3d Hybrid Parallel Delaunay Image-To-Mesh Conversion Algorithm For Distributed Shared Memory Architectures, Daming Feng, Christos Tsolakis, Andrey N. Chernikov, Nikos P. Chrisochoides

Computer Science Faculty Publications

In this paper, we present a scalable three dimensional hybrid parallel Delaunay image-to-mesh conversion algorithm (PDR.PODM) for distributed shared memory architectures. PDR.PODM is able to explore parallelism early in the mesh generation process because of the aggressive speculative approach employed by the Parallel Optimistic Delaunay Mesh generation algorithm (PODM). In addition, it decreases the communication overhead and improves data locality by making use of a data partitioning scheme offered by the Parallel Delaunay Refinement algorithm (PDR). PDR.PODM utilizes an octree structure to decompose the initial mesh and to distribute the bad elements to different octree leaves (subregions). A set of …

Saccharomyces Boulardii And Bismuth Subsalicylate As Low-Cost Interventions To Reduce The Duration And Severity Of Cholera, Johnathan Sheele, Jessica Cartowski, Angela Dart, Arjun Poddar, Shikha Gupta, Ajay Gupta

Computer Science Faculty Publications

We conducted a randomised single-blinded clinical trial of 100 cholera patients in Port-au-Prince, Haiti to determine if the probiotic Saccharomyces cerevisiae var. boulardii and the anti-diarrhoeal drug bismuth subsalicylate (BS) were able to reduce the duration and severity of cholera. Subjects received either: S. boulardii 250 mg, S. boulardii 250 mg capsule plus BS 524 mg tablet, BS 524 mg, or two placebo capsules every 6 hours alongside standard treatment for cholera. The length of hospitalisation plus the number and volume of emesis, stool and urine were recorded every 6 hours until the study subject was discharged (n=83), left against …

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 …

Learning By Doing - Energy Systems Management, Nima Shahriari, Adrian V. Gheorghe

Engineering Management & Systems Engineering Faculty Publications

Climate change concerns have confronted energy policy makers by unprecedented challenges in the 21st century. Revolution of renewable energy technologies, as well as more efficient energy systems, has been promising in the context of global warming. However, these technologies are not maturing and chaning. Consequently planning for development of these resources requires dealing with various multidisciplinary research questions such as financial feasibility of renewable energy projects. Nevertheless, there is considerable lack of education programs offering multidisciplinary approach for addressing the current energy challenges. Based on the 21st evolving energy landscape, an interdisciplinary graduate certificate course work was designed at Old …

High-Performance Simulations Of Coherent Synchrotron Radiation On Multicore Gpu And Cpu Platforms, B. Terzić, A. Godunov, K. Arumugam, D. Ranjan, M. Zubair

Physics Faculty Publications

Coherent synchrotron radiation (CSR) is an effect of self-interaction of an electron bunch as it traverses a curved path. It can cause a significant emittance degradation and microbunching. We present a new high-performance 2D, particle-in-cell code which uses massively parallel multicore GPU/GPU platforms to alleviate computational bottlenecks. The code formulates the CSR problem from first principles by using the retarded scalar and vector potentials to compute the self-interaction fields. The speedup due to the parallel implementation on GPU/CPU platforms exceeds three orders of magnitude, thereby bringing a previously intractable problem within reach. The accuracy of the code is verified against …

Tetrahedral Image-To-Mesh Conversion Software For Anatomic Modeling Of Arteriovenous Malformations, Fotis Drakopoulos, Ricardo Ortiz, Andinet Enquobahrie, Deanna Sasaki-Adams, Nikos Chrisochoides

Computer Science Faculty Publications

We describe a new implementation of an adaptive multi-tissue tetrahedral mesh generator targeting anatomic modeling of Arteriovenous Malformation (AVM) for surgical simulations. Our method, initially constructs an adaptive Body-Centered Cubic (BCC) mesh of high quality elements. Then, it deforms the mesh surfaces to their corresponding physical image boundaries, hence, improving the mesh fidelity and smoothness. Our deformation scheme, which builds upon the ITK toolkit, is based on the concept of energy minimization, and relies on a multi-material point-based registration. It uses non-connectivity patterns to implicitly control the number of the extracted feature points needed for the registration, and thus, adjusts …

Profiling Web Archives For Efficient Memento Query Routing, Sawood Alam, Michael L. Nelson, Herbert Van De Sompel, Lyudmila L. Balakireva, Harihar Shankar, David S. H. Rosenthal

Computer Science Faculty Publications

No abstract provided.

Deep Convolutional Neural Networks For Annotating Gene Expression Patterns In The Mouse Brain, Tao Zeng, Rongjian Li, Ravi Mukkamala, Jieping Ye, Shuiwang Ji

Computer Science Faculty Publications

Background: Profiling gene expression in brain structures at various spatial and temporal scales is essential to understanding how genes regulate the development of brain structures. The Allen Developing Mouse Brain Atlas provides high-resolution 3-D in situ hybridization (ISH) gene expression patterns in multiple developing stages of the mouse brain. Currently, the ISH images are annotated with anatomical terms manually. In this paper, we propose a computational approach to annotate gene expression pattern images in the mouse brain at various structural levels over the course of development.

Results: We applied deep convolutional neural network that was trained on a large set …

Characteristics Of Social Media Stories, Yasmin Ainoamany, Michele C. Weigle, Michael L. Nelson

Computer Science Faculty Publications

An emerging trend in social media is for users to create and publish "stories", or curated lists of web resources with the purpose of creating a particular narrative of interest to the user. While some stories on the web are automatically generated, such as Facebook’s "Year in Review", one of the most popular storytelling services is "Storify", which provides users with curation tools to select, arrange, and annotate stories with content from social media and the web at large. We would like to use tools like Storify to present automatically created summaries of archival collections. To support automatic story creation, …

Resilient And Trustworthy Dynamic Data-Driven Application Systems (Dddas) Services For Crisis Management Environments, Youakim Badr, Salim Hariti, Youssif Al-Nashif, Erik Blasch

Electrical & Computer Engineering Faculty Publications

Future crisis management systems needresilient and trustworthy infrastructures to quickly develop reliable applications and processes, andensure end-to-end security, trust, and privacy. Due to the multiplicity and diversity of involved actors, volumes of data, and heterogeneity of shared information;crisis management systems tend to be highly vulnerable and subjectto unforeseen incidents. As a result, the dependability of crisis management systems can be at risk. This paper presents a cloud-based resilient and trustworthy infrastructure (known as rDaaS) to quickly develop secure crisis management systems. The rDaaS integrates the Dynamic Data-Driven Application Systems (DDDAS) paradigm into a service-oriented architecture over cloud technology and provides …

A Robust Deep Model For Improved Classification Of Ad/Mci Patients, Feng Li, Loc Tran, Kim-Han Thung, Shuiwang Ji, Dinggang Shen, Jiang Li

Electrical & Computer Engineering Faculty Publications

Accurate classification of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI), plays a critical role in possibly preventing progression of memory impairment and improving quality of life for AD patients. Among many research tasks, it is of a particular interest to identify noninvasive imaging biomarkers for AD diagnosis. In this paper, we present a robust deep learning system to identify different progression stages of AD patients based on MRI and PET scans. We utilized the dropout technique to improve classical deep learning by preventing its weight coadaptation, which is a typical cause of overfitting in deep learning. …

Adaptive Graph Construction For Isomap Manifold Learning, Loc Tran, Zezhong Zheng, Guoquing Zhou, Jiang Li, Karen O. Egiazarian (Ed.), Sos S. Agaian (Ed.), Atanas P. Gotchev (Ed.)

Electrical & Computer Engineering Faculty Publications

Isomap is a classical manifold learning approach that preserves geodesic distance of nonlinear data sets. One of the main drawbacks of this method is that it is susceptible to leaking, where a shortcut appears between normally separated portions of a manifold. We propose an adaptive graph construction approach that is based upon the sparsity property of the ℓ₁ norm. The ℓ₁ enhanced graph construction method replaces k-nearest neighbors in the classical approach. The proposed algorithm is first tested on the data sets from the UCI data base repository which showed that the proposed approach performs better than …

The Multimodal Brain Tumor Image Segmentation Benchmark (Brats), Bjoern H. Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Khan M. Iftekharuddin, Syed M.S. Reza

Electrical & Computer Engineering Faculty Publications

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low-and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions …

Sparse Coding Based Dense Feature Representation Model For Hyperspectral Image Classification, Ender Oguslu, Guoqing Zhou, Zezhong Zheng, Khan Iftekharuddin, Jiang Li

Electrical & Computer Engineering Faculty Publications

We present a sparse coding based dense feature representation model (a preliminary version of the paper was presented at the SPIE Remote Sensing Conference, Dresden, Germany, 2013) for hyperspectral image (HSI) classification. The proposed method learns a new representation for each pixel in HSI through the following four steps: sub-band construction, dictionary learning, encoding, and feature selection. The new representation usually has a very high dimensionality requiring a large amount of computational resources. We applied the l₁/l_q regularized multiclass logistic regression technique to reduce the size of the new representation. We integrated the method with a linear …

A Comparative Study Of Two Prediction Models For Brain Tumor Progression, Deqi Zhou, Loc Tran, Jihong Wang, Jiang Li, Karen O. Egiazarian (Ed.), Sos S. Agaian (Ed.), Atanas P. Gotchev (Ed.)

Electrical & Computer Engineering Faculty Publications

MR diffusion tensor imaging (DTI) technique together with traditional T1 or T2 weighted MRI scans supplies rich information sources for brain cancer diagnoses. These images form large-scale, high-dimensional data sets. Due to the fact that significant correlations exist among these images, we assume low-dimensional geometry data structures (manifolds) are embedded in the high-dimensional space. Those manifolds might be hidden from radiologists because it is challenging for human experts to interpret high-dimensional data. Identification of the manifold is a critical step for successfully analyzing multimodal MR images.

We have developed various manifold learning algorithms (Tran et al. 2011; Tran et al. …

Engineering Analytics: Research Into The Governance Structure Needed To Integrate The Dominant Design Methodologies, Teddy Steven Cotter

Engineering Management & Systems Engineering Faculty Publications

In the ASEM-IAC 2014, Cotter (2014) explored the current state of engineering design, identified the dominate approaches to engineering design, discussed potential contributions from the new field of data analytics to engineering design, and proposed an Engineering Analytics framework that integrates the dominate engineering design approaches and data analytics within a human-intelligence/machine-intelligence (HI-MI) design architecture. This paper reports research applying ontological engineering to integrate the dominate engineering design methodologies into a systemic engineering design decision governance architecture.

Research Agenda Into Human-Intelligence/Machine-Intelligence Governance, Teddy Steven Cotter

Engineering Management & Systems Engineering Faculty Publications

Since the birth of modern artificial intelligence (AI) at the 1956 Dartmouth Conference, the AI community has pursued modeling and coding of human intelligence into AI reasoning processes (HI Þ MI). The Dartmouth Conference's fundamental assertion was that every aspect of human learning and intelligence could be so precisely described that it could be simulated in AI. With the exception of knowledge specific areas (such as IBM's Big Blue and a few others), sixty years later the AI community is not close to coding global human intelligence into AI. In parallel, the knowledge management (KM) community has pursued understanding of …