Physical Sciences and Mathematics Commons^™

Statistical And Machine Learning Analysis Of The Human Brain Functional Network In A Multi-Site Resting-State Functional Mri Database Framework, Oswaldo Artiles, Fahad Saeed, Ed.

School of Computing and Information Sciences

The human brain has a complex network structure that is non-random and multiscale. It consists of subsystems coupled by a nonlinear dynamic, enabling it to produce complex responses to various external inputs and self-organize. To understand the physical structure and specific brain functions, it is essential to comprehend the connectivity of the hundreds of billions of neurons in the human brain. Functional connectivity (FC) in modern neuroscience is the statistical temporal dependencies between neuronal activation events occurring in spatially separated brain regions. Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive imaging technique widely used in neuroscience to understand the …

High Performance Computing Algorithms For Accelerating Peptide Identification From Mass-Spectrometry Data Using Heterogeneous Supercomputers, Muhammad Haseeb, Fahad Saeed, Ed.

School of Computing and Information Sciences

Fast and accurate identification of peptides and proteins from the mass spectrometry (MS) data is a critical problem in modern systems biology. Database peptide search is the most commonly used computational method to identify peptide sequences from the MS data. In this method, giga-bytes of experimentally generated MS data are compared against tera-byte sized databases of theoretically simulated MS data resulting in a compute- and data-intensive problem requiring days or weeks of computational times on desktop machines. Existing serial and high performance computing (HPC) algorithms strive to accelerate and improve the computational efficiency of the search, but exhibit sub-optimal performances …

Time Series Modeling Of Cell Cycle Exit Identifies Brd4 Dependent Regulation Of Cerebellar Neurogenesis, Clara Penas, Maria E. Maloof, Vasileios Stathias, Jun Long, Sze Kiat Tan, Jose Mier, Yin Fang, Camilo Valdes, Jezabel Rodriguez-Blanco, Cheng-Ming Chiang, David J. Robbins, Daniel J. Liebl, Jae K. Lee, Mary E. Hatten, Jennifer Clarke, Nagi G. Ayad

School of Computing and Information Sciences

No abstract provided.

Enlace: A Combination Of Layer-Based Architecture And Wireless Communication For Emotion Monitoring In Healthcare, Leandro Y. Mano, Vincicius A. Barros, Luiz H. Nunes, Luana O. Sawada, Julio C. Estrella, Jo Ueyama

School of Computing and Information Sciences

Owing to the increase in the number of people with disabilities, as a result of either accidents or old age, there has been an increase in research studies in the area of ubiquitous computing and the Internet of Things. They are aimed at monitoring health, in an efficient and easily accessible way, as a means of managing and improving the quality of life of this section of the public. It also involves adopting a Health Homes policy based on the Internet of Things and applied in smart home environments. This is aimed at providing connectivity between the patients and their …

Auto-Asd-Network: A Technique Based On Deep Learning And Support Vector Machines For Diagnosing Autism Spectrum Disorder Using Fmri Data, Taban Eslami, Fahad Saeed

School of Computing and Information Sciences

Quantitative analysis of brain disorders such as Autism Spectrum Disorder (ASD) is an ongoing field of research. Machine learning and deep learning techniques have been playing an important role in automating the diagnosis of brain disorders by extracting discriminative features from the brain data. In this study, we propose a model called Auto-ASD-Network in order to classify subjects with Autism disorder from healthy subjects using only fMRI data. Our model consists of a multilayer perceptron (MLP) with two hidden layers. We use an algorithm called SMOTE for performing data augmentation in order to generate artificial data and avoid overfitting, which …

Graph Theoretic And Pearson Correlation-Based Discovery Of Network Biomarkers For Cancer, Raihanul Bari Tanvir, Tasmia Aqila, Mona Maharjan, Abdullah Al Mamun, Ananda Mohan Mondal

School of Computing and Information Sciences

Two graph theoretic concepts—clique and bipartite graphs—are explored to identify the network biomarkers for cancer at the gene network level. The rationale is that a group of genes work together by forming a cluster or a clique-like structures to initiate a cancer. After initiation, the disease signal goes to the next group of genes related to the second stage of a cancer, which can be represented as a bipartite graph. In other words, bipartite graphs represent the cross-talk among the genes between two disease stages. To prove this hypothesis, gene expression values for three cancers— breast invasive carcinoma (BRCA), colorectal …

Ckmi: Comprehensive Key Management Infrastructure Design For Industrial Automation And Control Systems, T.C. Pramod, Thejas G.S., S.S. Iyengar, N. R. Sunitha

School of Computing and Information Sciences

Industrial Automation and Control Systems (IACS) are broadly utilized in critical infrastructures for monitoring and controlling the industrial processes remotely. The real-time transmissions in such systems provoke security breaches. Many security breaches have been reported impacting society severely. Hence, it is essential to achieve secure communication between the devices for creating a secure environment. For this to be effective, the keys used for secure communication must be protected against unauthorized disclosure, misuse, alteration or loss, which can be taken care of by a Key Management Infrastructure. In this paper, by considering the generic industrial automation network, a comprehensive key management …

Dynamic Interaction Network Inference From Longitudinal Microbiome Data, Jose Lugo-Martinez, Daniel Ruiz-Perez, Giri Narasimhan, Ziv Bar-Joseph

School of Computing and Information Sciences

Background

Several studies have focused on the microbiota living in environmental niches including human body sites. In many of these studies, researchers collect longitudinal data with the goal of understanding not only just the composition of the microbiome but also the interactions between the different taxa. However, analysis of such data is challenging and very few methods have been developed to reconstruct dynamic models from time series microbiome data.

Results

Here, we present a computational pipeline that enables the integration of data across individuals for the reconstruction of such models. Our pipeline starts by aligning the data collected for all …

Lbe: A Computational Load Balancing Algorithm For Speeding Up Parallel Peptide Search In Mass-Spectrometry Based Proteomics, Muhammad Haseeb, Fatima Afzali, Fahad Saeed

School of Computing and Information Sciences

The most commonly employed method for peptide identification in mass-spectrometry based proteomics involves comparing experimentally obtained tandem MS/MS spectra against a set of theoretical MS/MS spectra. The theoretical MS/MS spectra data are predicted using protein sequence database. Most state-of-the-art peptide search algorithms index theoretical spectra data to quickly filter-in the relevant (similar) indexed spectra when searching an experimental MS/MS spectrum. Data filtration substantially reduces the required number of computationally expensive spectrum-to-spectrum comparison operations. However, the number of predicted (and indexed) theoretical spectra grows exponentially with increase in posttranslational modifications creating a memory and I/O bottleneck. In this paper, we present …

Gpu-Dfc: A Gpu-Based Parallel Algorithm For Computing Dynamic-Functional Connectivity Of Big Fmri Data, Taban Eslami, Fahad Saeed

School of Computing and Information Sciences

Studying dynamic-functional connectivity (DFC) using fMRI data of the brain gives much richer information to neuroscientists than studying the brain as a static entity. Mining of dynamic connectivity graphs from these brain studies can be used to classify diseased versus healthy brains. However, constructing and mining dynamic-functional connectivity graphs of the brain can be time consuming due to size of fMRI data. In this paper, we propose a highly scalable GPU-based parallel algorithm called GPU-DFC for computing dynamic-functional connectivity of fMRI data both at region and voxel level. Our algorithm exploits sparsification of correlation matrix and stores them in CSR …

Parallel Sampling-Pipeline For Indefinite Stream Of Heterogeneous Graphs Using Opencl For Fpgas, Muhammad Usman Tariq, Fahad Saeed

School of Computing and Information Sciences

In the field of data science, a huge amount of data, generally represented as graphs, needs to be processed and analyzed. It is of utmost importance that this data be processed swiftly and efficiently to save time and energy. The volume and velocity of data, along with irregular access patterns in graph data structures, pose challenges in terms of analysis and processing. Further, a big chunk of time and energy is spent on analyzing these graphs on large compute clusters and/or data-centers. Filtering and refining of data using graph sampling techniques are one of the most effective ways to speed …

Autonomous Pipeline Monitoring And Maintenance System: A Rfid-Based Approach, Jong-Hoon Kim, Gokarna Sharma, Noureddine Boudriga, S.S. Iyengar, Nagarajan Prabakar

School of Computing and Information Sciences

Pipeline networks are one of the key infrastructures of our modern life. Proactive monitoring and frequent inspection of pipeline networks are very important for sustaining their safe and efficient functionalities. Existing monitoring and maintenance approaches are costly and inefficient because pipelines can be installed in large scale and in an inaccessible and hazardous environment. To overcome these challenges, we propose a novel Radio Frequency IDentification (RFID)-based Autonomous Maintenance system for Pipelines, called RAMP, which combines robotic, sensing, and RFID technologies for efficient and accurate inspection, corrective reparation, and precise geo-location information. RAMP can provide not only economical and scalable remedy …

A Multiple Resonant Frequencies Circular Reconfigurable Antenna Investigated With Wireless Powering In A Concrete Block, Shishir Shanker Punjala, Nikki Pissinou, Kia Makki

School of Computing and Information Sciences

A novel broadband reconfigurable antenna design that can cover different frequency bands is presented.This antenna has multiple resonant frequencies. The reflection coefficient graphs for this antenna are presented in this paper. The new proposed design was investigated along with RFMEMS switches and the results are also presented. Investigations were carried out to check the efficiency of the antenna in the wireless powering domain. The antenna was placed in a concrete block and its result comparison to that of a dipole antenna is also presented in this paper.

Diffeomorphism Spline, Wei Zeng, Muhammad Razib, Abdur Bin Shahid

School of Computing and Information Sciences

Conventional splines offer powerful means for modeling surfaces and volumes in three-dimensional Euclidean space. A one-dimensional quaternion spline has been applied for animation purpose, where the splines are defined to model a one-dimensional submanifold in the three-dimensional Lie group. Given two surfaces, all of the diffeomorphisms between them form an infinite dimensional manifold, the so-called diffeomorphism space. In this work, we propose a novel scheme to model finite dimensional submanifolds in the diffeomorphism space by generalizing conventional splines. According to quasiconformal geometry theorem, each diffeomorphism determines a Beltrami differential on the source surface. Inversely, the diffeomorphism is determined by its …

Statistical Methods In Ai: Rare Event Learning Using Associative Rules And Higher-Order Statistics, V. Iyer, S. Shetty, S.S. Iyengar

School of Computing and Information Sciences

No abstract provided.

Non-Invasive Clinical Parameters For The Prediction Of Urodynamic Bladder Outlet Obstruction: Analysis Using Causal Bayesian Networks, Myong Kim, Abhilash Cheeti, Changwon Yoo, Minsoo Choo, Jae-Seung Paick, Seung-June Oh

School of Computing and Information Sciences

Purpose: To identify non-invasive clinical parameters to predict urodynamic bladder outlet obstruction (BOO) in patients with benign prostatic hyperplasia (BPH) using causal Bayesian networks (CBN). Subjects and Methods: From October 2004 to August 2013, 1,381 eligible BPH patients with complete data were selected for analysis. The following clinical variables were considered: age, total prostate volume (TPV), transition zone volume (TZV), prostate specific antigen (PSA), maximum flow rate (Qmax), and post-void residual volume (PVR) on uroflowmetry, and International Prostate Symptom Score (IPSS). Among these variables, the independent predictors of BOO were selected using the CBN model. The predictive performance of the …

Challenges And Directions In Formalizing The Semantics Of Modeling Languages, Barrett R. Bryant, Jeff Gray, Marjan Mernik, Peter J. Clarke, Robert B. France, Gabor Karsai

School of Computing and Information Sciences

Developing software from models is a growing practice and there exist many model-based tools (e.g., editors, interpreters, debuggers, and simulators) for supporting model-driven engineering. Even though these tools facilitate the automation of software engineering tasks and activities, such tools are typically engineered manually. However, many of these tools have a common semantic foundation centered around an underlying modeling language, which would make it possible to automate their development if the modeling language specification were formalized. Even though there has been much work in formalizing programming languages, with many successful tools constructed using such formalisms, there has been little work in …

Gene Selection Algorithm By Combining Relieff And Mrmr, Yi Zhang, Chris Ding, Tao Li

School of Computing and Information Sciences

Background: Gene expression data usually contains a large number of genes, but a small number of samples. Feature selection for gene expression data aims at finding a set of genes that best discriminate biological samples of different types. In this paper, we present a two-stage selection algorithm by combining ReliefF and mRMR: In the first stage, ReliefF is applied to find a candidate gene set; In the second stage, mRMR method is applied to directly and explicitly reduce redundancy for selecting a compact yet effective gene subset from the candidate set. Results: We perform comprehensive experiments to compare the mRMR-ReliefF …

Notes On Sufficient Conditions For A Graph To Be Hamiltonian, Michael Joseph Paul, Carmen Baytan Shershin, Anthony Connors Shershin

School of Computing and Information Sciences

The first part of this paper deals with an extension of Dirac's Theorem to directed graphs. It is related to a result often referred to as the Ghouila-Houri Theorem. Here we show that the requirement of being strongly connected in the hypothesis of the Ghouila-Houri Theorem is redundant.

The Second part of the paper shows that a condition on the number of edges for a graph to be hamiltonian implies Ore's condition on the degrees of the vertices.