Physical Sciences and Mathematics Commons^™

An Autoencoder-Based Deep Learning Method For Genotype Imputation, Meng Song, Jonathan Greenbaum, Joseph Luttrell Iv, Weihua Zhou, Chong Wu, Zhe Luo, Chuan Qiu, Lan Juan Zhao, Kuan-Jui Su, Qing Tian, Hui Shen, Huixiao Hong, Ping Gong, Xinghua Shi, Hong-Wen Deng, Chaoyang Zhang

Faculty Publications

Genotype imputation has a wide range of applications in genome-wide association study (GWAS), including increasing the statistical power of association tests, discovering trait-associated loci in meta-analyses, and prioritizing causal variants with fine-mapping. In recent years, deep learning (DL) based methods, such as sparse convolutional denoising autoencoder (SCDA), have been developed for genotype imputation. However, it remains a challenging task to optimize the learning process in DL-based methods to achieve high imputation accuracy. To address this challenge, we have developed a convolutional autoencoder (AE) model for genotype imputation and implemented a customized training loop by modifying the training process with a …

St-V-Net: Incorporating Shape Prior Into Convolutional Neural Netwoks For Proximal Femur Segmentation, Chen Zhao, Joyce H. Keyak, Jinshan Tang, Tadashi S. Kaneko, Sundeep Khosla, Shreyasee Amin, Elizabeth J. Atkinson, Lan-Juan Zhao, Michael J. Serou, Chaoyang Zhang, Hui Shen, Hong-Wen Deng, Weihua Zhou

Faculty Publications

We aim to develop a deep-learning-based method for automatic proximal femur segmentation in quantitative computed tomography (QCT) images. We proposed a spatial transformation V-Net (ST-V-Net), which contains a V-Net and a spatial transform network (STN) to extract the proximal femur from QCT images. The STN incorporates a shape prior into the segmentation network as a constraint and guidance for model training, which improves model performance and accelerates model convergence. Meanwhile, a multi-stage training strategy is adopted to fine-tune the weights of the ST-V-Net. We performed experiments using a QCT dataset which included 397 QCT subjects. During the experiments for the …

Privacy-Preserving Non-Participatory Surveillance System For Covid-19-Like Pandemics, Mahmoud Nabil, Ahmed Sherif, Mohamed Mahmoud, Waleed Alsmary, Maazen Alsabaan

Faculty Publications

COVID-19 pandemic has revealed a pressing need for an effective surveillance system to control the spread of infection. However, the existing systems are run by the people’s smartphones and without a strong participation from the people, the systems become ineffective. Moreover, these systems can be misused to spy on people and breach their privacy. Due to recent privacy breaches, people became anxious about their privacy, and without privacy reassurance, the people may not accept the systems. In this paper, we propose a non-participatory privacy-preserving surveillance system for COVID-19-like pandemics. The system aims to control the spread of COVID-19 infection without …

Analyze Informant-Based Questionnaire For The Early Diagnosis Of Senile Dementia Using Deep Learning, Fubao Zhu, Xiaonan Li, Daniel Mcgonigle, Haipeng Tang, Zhuo He, Chaoyang Zhang, Guang-Uei Hung, Pai-Yi Chu, Weihua Zhou

Faculty Publications

Objective: This paper proposes a multiclass deep learning method for the classification of dementia using an informant-based questionnaire.

Methods: A deep neural network classification model based on Keras framework is proposed in this paper. To evaluate the advantages of our proposed method, we compared the performance of our model with industry-standard machine learning approaches. We enrolled 6,701 individuals, which were randomly divided into training data sets (6030 participants) and test data sets (671 participants). We evaluated each diagnostic model in the test set using accuracy, precision, recall, and F1-Score.

Results: Compared with the seven conventional machine learning …

Rf Energy Harvesting Wireless Communication: Rf Environment, Device Hardware And Practical Issues, Yu Luo, Lina Pu, Guodong Wang, Yanxiao Zhao

Faculty Publications

Radio frequency (RF) based wireless power transfer provides an attractive solution to extend the lifetime of power-constrained wireless sensor networks. Through harvesting RF energy from surrounding environments or dedicated energy sources, low-power wireless devices can be self-sustaining and environment-friendly. These features make the RF energy harvesting wireless communication (RF-EHWC) technique attractive to a wide range of applications. The objective of this article is to investigate the latest research activities on the practical RF-EHWC design. The distribution of RF energy in the real environment, the hardware design of RF-EHWC devices and the practical issues in the implementation of RF-EHWC networks are …

Development And Validation Of A New Method To Diagnose Apical Hypertrophic Cardiomyopathy By Gated Single-Photon Emission Computed Tomography Myocardial Perfusion Imaging, Yanli Zhou, Dianfu Li, Haipeng Tang, Yi Xu, Cheng Wang, Zhixin Jiang, Fang Xu, Zhongqiang Zhao, Chunxiang Li, Shaojie Tang, Lijun Tang, Weihua Zhou

Faculty Publications

Aim The aim of this study is to develop and validate a new method to diagnose apical hypertrophic cardiomyopathy (AHCM) by the integral quantitative analysis of myocardial perfusion and wall thickening from gated single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI).

Patients and methods Twenty-two consecutive patients, who showed T wave inversion of at least 3 mm in precordial leads and sinus rhythm in ECG, were enrolled. All the patients underwent cardiac magnetic resonance (CMR), gated rest SPECT MPI and echocardiography. According to CMR diagnostic results, 13 patients were categorized as in the AHCM group and the remaining nine …

A Perfect Tool For Comprehensive Evaluation Of Myocardial Perfusion And Function: Stress Pet Imaging, Guang-Uei Hung, Weihua Zhou, Ji Chen

Faculty Publications

No abstract provided.

Receiver-Initiated Handshaking Mac Based On Traffic Estimation For Underwater Sensor Networks, Yuan Dong, Lina Pu, Yu Luo, Zheng Peng, Haining Mo, Yun Meng, Yi Zhao, Yuzhi Zhang

Faculty Publications

In underwater sensor networks (UWSNs), the unique characteristics of acoustic channels have posed great challenges for the design of medium access control (MAC) protocols. The long propagation delay problem has been widely explored in recent literature. However, the long preamble problem with acoustic modems revealed in real experiments brings new challenges to underwater MAC design. The overhead of control messages in handshaking-based protocols becomes significant due to the long preamble in underwater acoustic modems. To address this problem, we advocate the receiver-initiated handshaking method with parallel reservation to improve the handshaking efficiency. Despite some existing works along this direction, the …

Gogo: An Improved Algorithm To Measure The Semantic Similarity Between Gene Ontology Terms, Chenguang Zhao, Zheng Wang

Student Publications

Measuring the semantic similarity between Gene Ontology (GO) terms is an essential step in functional bioinformatics research. We implemented a software named GOGO for calculating the semantic similarity between GO terms. GOGO has the advantages of both information-content-based and hybrid methods, such as Resnik’s and Wang’s methods. Moreover, GOGO is relatively fast and does not need to calculate information content (IC) from a large gene annotation corpus but still has the advantage of using IC. This is achieved by considering the number of children nodes in the GO directed acyclic graphs when calculating the semantic contribution of an ancestor node …

Malware Analysis On Android Using Supervised Machine Learning Techniques, Md Shohel Rana, Andrew H. Sung

Faculty Publications

In recent years, a widespread research is conducted with the growth of malware resulted in the domain of malware analysis and detection in Android devices. Android, a mobile-based operating system currently having more than one billion active users with a high market impact that have inspired the expansion of malware by cyber criminals. Android implements a different architecture and security controls to solve the problems caused by malware, such as unique user ID (UID) for each application, system permissions, and its distribution platform Google Play. There are numerous ways to violate that fortification, and how the complexity of creating a …

Left Ventricular Mechanical Dyssynchrony For Cad Diagnosis: Does It Have Incremental Clinical Values?, Zhixin Jiang, Weihua Zhou

Faculty Publications

No abstract provided.

Reliable Delay Based Algorithm To Boost Puf Security Against Modeling Attacks, Fathi Amsaad, Mohammaed Niamat, Amer Dawoud, Selcuk Kose

Faculty Publications

Silicon Physical Unclonable Functions (sPUFs) are one of the security primitives and state-of-the-art topics in hardware-oriented security and trust research. This paper presents an efficient and dynamic ring oscillator PUFs (d-ROPUFs) technique to improve sPUFs security against modeling attacks. In addition to enhancing the Entropy of weak ROPUF design, experimental results show that the proposed d-ROPUF technique allows the generation of larger and updated challenge-response pairs (CRP space) compared with simple ROPUF. Additionally, an innovative hardware-oriented security algorithm, namely, the Optimal Time Delay Algorithm (OTDA), is proposed. It is demonstrated that the OTDA algorithm significantly improves PUF reliability under varying …

Assessing Relevance Of Tweets For Risk Communication, Xiaohui Liu, Bandana Kar, Chaoyang Zhang, David M. Cochran

Faculty Publications

Although Twitter is used for emergency management activities, the relevance of tweets during a hazard event is still open to debate. In this study, six different computational (i.e. Natural Language Processing) and spatiotemporal analytical approaches were implemented to assess the relevance of risk information extracted from tweets obtained during the 2013 Colorado flood event. Primarily, tweets containing information about the flooding events and its impacts were analysed. Examination of the relationships between tweet volume and its content with precipitation amount, damage extent, and official reports revealed that relevant tweets provided information about the event and its impacts rather than any …

Accuracy Analysis Comparison Of Supervised Classification Methods For Anomaly Detection On Levees Using Sar Imagery, Ramakalavathi Marapareddy, James V. Aanstoos, Nicolas H. Younan

Faculty Publications

This paper analyzes the use of a synthetic aperture radar (SAR) imagery to support levee condition assessment by detecting potential slide areas in an efficient and cost-effective manner. Levees are prone to a failure in the form of internal erosion within the earthen structure and landslides (also called slough or slump slides). If not repaired, slough slides may lead to levee failures. In this paper, we compare the accuracy of the supervised classification methods minimum distance (MD) using Euclidean and Mahalanobis distance, support vector machine (SVM), and maximum likelihood (ML), using SAR technology to detect slough slides on earthen levees. …

Exact And Heuristic Algorithms For Risk-Aware Stochastic Physical Search, Daniel S. Brown, Jeffrey Hudack, Nathaniel Gemelli, Bikramjit Banerjee

Faculty Publications

We consider an intelligent agent seeking to obtain an item from one of several physical locations, where the cost to obtain the item at each location is stochastic. We study risk-aware stochastic physical search (RA-SPS), where both the cost to travel and the cost to obtain the item are taken from the same budget and where the objective is to maximize the probability of success while minimizing the required budget. This type of problem models many task-planning scenarios, such as space exploration, shopping, or surveillance. In these types of scenarios, the actual cost of completing an objective at a location …

An Ensemble Multilabel Classification For Disease Risk Prediction, Runzhi Li, Wei Liu, Yusong Lin, Hongling Zhao, Chaoyang Zhang

Faculty Publications

It is important to identify and prevent disease risk as early as possible through regular physical examinations. We formulate the disease risk prediction into a multilabel classification problem. A novel Ensemble Label Power-set Pruned datasets Joint Decomposition (ELPPJD) method is proposed in this work. First, we transform the multilabel classification into a multiclass classification. Then, we propose the pruned datasets and joint decomposition methods to deal with the imbalance learning problem. Two strategies size balanced (SB) and label similarity (LS) are designed to decompose the training dataset. In the experiments, the dataset is from the real physical examination records. We …

A Supervised Classification Method For Levee Slide Detection Using Complex Synthetic Aperture Radar Imagery, Ramakalavathi Marapareddy, James V. Aanstoos, Nicolas H. Younan

Faculty Publications

The dynamics of surface and sub-surface water events can lead to slope instability, resulting in anomalies such as slough slides on earthen levees. Early detection of these anomalies by a remote sensing approach could save time versus direct assessment. We have implemented a supervised Mahalanobis distance classification algorithm for the detection of slough slides on levees using complex polarimetric Synthetic Aperture Radar (polSAR) data. The classifier output was followed by a spatial majority filter post-processing step that improved the accuracy. The effectiveness of the algorithm is demonstrated using fully quad-polarimetric L-band Synthetic Aperture Radar (SAR) imagery from the NASA Jet …

Multi-Agent Reinforcement Learning As A Rehearsal For Decentralized Planning, Landon Kraemer, Bikramjit Banerjee

Faculty Publications

Decentralized partially observable Markov decision processes (Dec-POMDPs) are a powerful tool for modeling multi-agent planning and decision-making under uncertainty. Prevalent Dec-POMDP solution techniques require centralized computation given full knowledge of the underlying model. Multi-agent reinforcement learning (MARL) based approaches have been recently proposed for distributed solution of Dec-POMDPs without full prior knowledge of the model, but these methods assume that conditions during learning and policy execution are identical. In some practical scenarios this may not be the case. We propose a novel MARL approach in which agents are allowed to rehearse with information that will not be available during policy …

Predicting Dna Methylation State Of Cpg Dinucleotide Using Genome Topological Features And Deep Networks, Yiheng Wang, Tong Liu, Dong Xu, Huidong Shi, Chaoyang Zhang, Yin Yuan Mo, Zheng Wang

Faculty Publications

The hypo- or hyper-methylation of the human genome is one of the epigenetic features of leukemia. However, experimental approaches have only determined the methylation state of a small portion of the human genome. We developed deep learning based (stacked denoising autoencoders, or SdAs) software named "DeepMethyl" to predict the methylation state of DNA CpG dinucleotides using features inferred from three-dimensional genome topology (based on Hi-C) and DNA sequence patterns. We used the experimental data from immortalised myelogenous leukemia (K562) and healthy lymphoblastoid (GM12878) cell lines to train the learning models and assess prediction performance. We have tested various SdA architectures …

Benchmarking Deep Networks For Predicting Residue-Specific Quality Of Individual Protein Models In Casp11, Tong Liu, Yiheng Wang, Jesse Eickholt, Zheng Wang

Faculty Publications

Quality assessment of a protein model is to predict the absolute or relative quality of a protein model using computational methods before the native structure is available. Single-model methods only need one model as input and can predict the absolute residue-specific quality of an individual model. Here, we have developed four novel single-model methods (Wang_deep_1, Wang_deep_2, Wang_deep_3, and Wang_SVM) based on stacked denoising autoencoders (SdAs) and support vector machines (SVMs). We evaluated these four methods along with six other methods participating in CASP11 at the global and local levels using Pearson’s correlation coefficients and ROC analysis. As for residue-specific quality …

Differential Effects Of Munc18s On Multiple Degranulation-Relevant Trans-Snare Complexes, Hao Xu, Matthew Grant Arnold, Sushmitha Vijay Kumar

Faculty Publications

Mast cell exocytosis, which includes compound degranulation and vesicle-associated piecemeal degranulation, requires multiple Q- and R- SNAREs. It is not clear how these SNAREs pair to form functional trans-SNARE complexes and how these trans-SNARE complexes are selectively regulated for fusion. Here we undertake a comprehensive examination of the capacity of two Q-SNARE subcomplexes (syntaxin3/SNAP-23 and syntaxin4/SNAP-23) to form fusogenic trans-SNARE complexes with each of the four granule-borne R-SNAREs (VAMP2, 3, 7, 8). We report the identification of at least six distinct trans-SNARE complexes under enhanced tethering conditions: i) VAMP2/syntaxin3/SNAP-23, ii) VAMP2/syntaxin4/SNAP-23, iii) VAMP3/syntaxin3/SNAP-23, iv) VAMP3/syntaxin4/SNAP-23, v) VAMP8/syntaxin3/SNAP-23, and vi) VAMP8/syntaxin4/SNAP-23. …

A Kansa-Radial Basis Function Method For Elliptic Boundary Value Problems In Annular Domains, Xiao Yan Liu, Andreas Karageorghis, C. S. Chen

Faculty Publications

We employ a Kansa-radial basis function (RBF) method for the numerical solution of elliptic boundary value problems in annular domains. This discretization leads, with an appropriate selection of collocation points and for any choice of RBF, to linear systems in which the matrices possess block circulant structures. These linear systems can be solved efficiently using matrix decomposition algorithms and fast Fourier transforms. A suitable value for the shape parameter in the various RBFs used is found using the leave-one-out cross validation algorithm. In particular, we consider problems governed by the Poisson equation, the inhomogeneous biharmonic equation and the inhomogeneous Cauchy–Navier …

Seqassist: A Novel Toolkit For Preliminary Analysis Of Next-Generation Sequencing Data, Yan Peng, Andrew S. Maxwell, Natalie D. Barker, Jennifer G. Laird, Alan J. Kennedy, Nan Wang, Chaoyang Zhang, Ping Gong

Faculty Publications

Background: While next-generation sequencing (NGS) technologies are rapidly advancing, an area that lags behind is the development of efficient and user-friendly tools for preliminary analysis of massive NGS data. As an effort to fill this gap to keep up with the fast pace of technological advancement and to accelerate data-to-results turnaround, we developed a novel software package named SeqAssist ("Sequencing Assistant" or SA).

Results: SeqAssist takes NGS-generated FASTQ files as the input, employs the BWA-MEM aligner for sequence alignment, and aims to provide a quick overview and basic statistics of NGS data. It consists of three separate workflows: …

Identification Of Biomarkers That Distinguish Chemical Contaminants Based On Gene Expression Profiles, Xiaomou Wei, Junmei Ai, Youping Deng, Xin Guan, David R. Johnson, Choo Y. Ang, Chaoyang Zhang, Edward J. Perkins

Faculty Publications

Background: High throughput transcriptomics profiles such as those generated using microarrays have been useful in identifying biomarkers for different classification and toxicity prediction purposes. Here, we investigated the use of microarrays to predict chemical toxicants and their possible mechanisms of action.

Results: In this study, in vitro cultures of primary rat hepatocytes were exposed to 105 chemicals and vehicle controls, representing 14 compound classes. We comprehensively compared various normalization of gene expression profiles, feature selection and classification algorithms for the classification of these 105 chemicals into14 compound classes. We found that normalization had little effect on the averaged …

Effects Of Stereo And Screen Size On The Legibility Of Three-Dimensional Streamtube Visualization, Jian Chen, Haipeng Cai, Alexander P. Auchus, David H. Laidlaw

Faculty Publications

We report the impact of display characteristics (stereo and size) on task performance in diffusion magnetic resonance imaging (DMRI) in a user study with 12 participants. The hypotheses were that (1) adding stereo and increasing display size would improve task accuracy and reduce completion time, and (2) the greater the complexity of a spatial task, the greater the benefits of an improved display. Thus we expected to see greater performance gains when detailed visual reasoning was required. Participants used dense streamtube visualizations to perform five representative tasks: (1) determine the higher average fractional anisotropy (FA) values between two regions, (2) …

Learning The Structure Of Gene Regulatory Networks From Time Series Gene Expression Data, Haoni Li, Nan Wang, Ping Gong, Edward J. Perkins, Chaoyang Zhang

Faculty Publications

Background: Dynamic Bayesian Network (DBN) is an approach widely used for reconstruction of gene regulatory networks from time-series microarray data. Its performance in network reconstruction depends on a structure learning algorithm. REVEAL (REVerse Engineering ALgorithm) is one of the algorithms implemented for learning DBN structure and used to reconstruct gene regulatory networks (GRN). However, the two-stage temporal Bayes network (2TBN) structure of DBN that specifies correlation between time slices cannot be obtained by score metrics used in REVEAL.

Methods: In this paper, we study a more sophisticated score function for DBN first proposed by Nir Friedman for stationary …

Nuclear Pairing Reduction Due To Rotation And Blocking, X. Wu, Z.H. Zhang, J.Y. Zeng, Y.A. Lei

Faculty Publications

Nuclear pairing gaps of normally deformed and superdeformed nuclei are investigated using the particle-number-conserving (PNC) formalism for the cranked shell model, in which the blocking effects are treated exactly. Both rotational frequency omega dependence and seniority (number of unpaired particles) nu dependence of the pairing gap (Delta) over tilde are investigated. For the ground-state bands of even-even nuclei, PNC calculations show that, in general, (Delta) over tilde decreases with increasing omega, but the omega dependence is much weaker than that calculated by the number-projected Hartree-Fock-Bogolyubov approach. For the multiquasiparticle bands (seniority nu > 2), the pairing gaps stay almost omega independent. …

Roughening, Deroughening, And Nonuniversal Scaling Of The Interface Width In Electrophoretic Deposition Of Polymer Chains, Frank W. Bentrem, Ras B. Pandey, Fereydoon Family

Faculty Publications

Growth and roughness of the interface of deposited polymer chains driven by a field onto an impenetrable adsorbing surface are studied by computer simulations in (2 + 1) dimensions. The evolution of the interface width W shows a crossover from short-time growth described by the exponent beta(1) to a long-time growth with exponent beta(2) (>beta(1)) Tne saturated width increases, i.e., the interface roughens, with the molecular weight L-c, but the roughness exponent alpha (from W-s similar to L-alpha) becomes negative in contrast to models for particle deposition; cr depends on the chain length-a nonuniversal scaling with the substrate length …

Almost Block Diagonal Linear Systems: Sequential And Parallel Solution Techniques, And Applications, P. Amodio, J.R. Cash, G. Roussos, R.W. Wright, G. Fairweather, I. Gladwell, G.L. Kraut, M. Paprzycki

Faculty Publications

Almost block diagonal (ABD) linear systems arise in a variety of contexts, specifically in numerical methods for two-point boundary value problems for ordinary differential equations and in related partial differential equation problems. The stable, efficient sequential solution of ABDs has received much attention over the last fifteen years and the parallel solution more recently. We survey the fields of application with emphasis on how ABDs and bordered ABDs (BABDs) arise. We outline most known direct solution techniques, both sequential and parallel, and discuss the comparative efficiency of the parallel methods. Finally, we examine parallel iterative methods for solving BABD systems. …

Parallel Linear Congruential Generators With Prime Moduli, Michael Mascagni

Faculty Publications

Linear congruential generators (LCGs) remain the most popular method of pseudorandom number generation on digital computers. Ease of implementation has favored implementing LCGs with power-of-two moduli. However, prime modulus LCGs are superior in quality to power-of-two modulus LCGs, and the use of a Mersenne prime minimizes the computational cost of generation. When implemented for parallel computation, quality becomes an even more compelling issue. We use a full-period exponential sum as the measure of stream independence and present a method for producing provably independent streams of LCGs in parallel by utilizing an explicit parameterization of all of the primitive elements module …