Physical Sciences and Mathematics Commons^™

Remote Sensing Of Forests Using Discrete Return Airborne Lidar, Hamid Hamraz, Marco A. Contreras

Forestry and Natural Resources Faculty Publications

Airborne discrete return light detection and ranging (LiDAR) point clouds covering forested areas can be processed to segment individual trees and retrieve their morphological attributes. Segmenting individual trees in natural deciduous forests, however, remained a challenge because of the complex and multi-layered canopy. In this chapter, we present (i) a robust segmentation method that avoids a priori assumptions about the canopy structure, (ii) a vertical canopy stratification procedure that improves segmentation of understory trees, (iii) an occlusion model for estimating the point density of each canopy stratum, and (iv) a distributed computing approach for efficient processing at the forest level. …

A Brief Overview Of Intelligent Mobility Management For Future Wireless Mobile Networks, Ilsun You, Yuh-Shyan Chen, Sherali Zeadally, Fei Song

Information Science Faculty Publications

No abstract provided.

Ordinal Convolutional Neural Networks For Predicting Rdoc Positive Valence Psychiatric Symptom Severity Scores, Anthony Rios, Ramakanth Kavuluru

Computer Science Faculty Publications

Background—The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) provided a set of 1000 neuropsychiatric notes to participants as part of a competition to predict psychiatric symptom severity scores. This paper summarizes our methods, results, and experiences based on our participation in the second track of the shared task.

Objective—Classical methods of text classification usually fall into one of three problem types: binary, multi-class, and multi-label classification. In this effort, we study ordinal regression problems with text data where misclassifications are penalized differently based on how far apart the ground truth and model predictions are …

Predicting Mental Conditions Based On "History Of Present Illness" In Psychiatric Notes With Deep Neural Networks, Tung Tran, Ramakanth Kavuluru

Computer Science Faculty Publications

Background—Applications of natural language processing to mental health notes are not common given the sensitive nature of the associated narratives. The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) changed this scenario by providing the first set of neuropsychiatric notes to participants. This study summarizes our efforts and results in proposing a novel data use case for this dataset as part of the third track in this shared task.

Objective—We explore the feasibility and effectiveness of predicting a set of common mental conditions a patient has based on the short textual description of patient’s history …

Single Versus Concurrent Systems: Nominal Classification In Mian, Greville G. Corbett, Sebastian Fedden, Raphael Finkel

Computer Science Faculty Publications

The Papuan language Mian allows us to refine the typology of nominal classification. Mian has two candidate classification systems, differing completely in their formal realization but overlapping considerably in their semantics. To determine whether to analyse Mian as a single system or concurrent systems we adopt a canonical approach. Our criteria – orthogonality of the systems (we give a precise measure), semantic compositionality, morphosyntactic alignment, distribution across parts of speech, exponence, and interaction with other features – point mainly to an analysis as concurrent systems. We thus improve our analysis of Mian and make progress with the typology of nominal …

The Transformation Of Science With Hpc, Big Data, And Ai, Jeffrey Kirk

Commonwealth Computational Summit

High performance computing has matured into an indispensable tool for not only academic research and government labs and agencies, but also for many industry sectors: energy, manufacturing, healthcare, financial services, even digital content creation. More recently, the advent of Big Data has enabled the use of HPC techniques for large scale data analysis, expanding the scope of HPC and the reach of it into more research and enterprise use cases. Since 2012, a new regime of data-driven analytics, deep learning, has erupted in popularity, fueled by both the massive performance increases in HPC technologies and in the explosive rate of …

Harnessing The Data Revolution, Chaitan Baru

Commonwealth Computational Summit

Harnessing Data for 21st Century Science and Engineering (aka Harnessing the Data Revolution, HDR) is one of NSF's six "Big Research Ideas," aimed at supporting fundamental research in data science and engineering; developing a cohesive, federated approach to the research data infrastructure needed to power this revolution; and developing of a 21st-century data-capable workforce. HDR will enable new modes of data-driven discovery allowing researchers to ask and answer new questions in frontier science and engineering, generate new knowledge and understanding by working with domain experts, and accelerate discovery and innovation. This initiative builds on NSF's history of data science investments. …

Tell Me Why? Tell Me More! Explaining Predictions, Iterated Learning Bias, And Counter-Polarization In Big Data Discovery Models, Olfa Nasraoui

Commonwealth Computational Summit

Outline:

What can go Wrong in Machine Learning?

• Unfair Machine Learning
• Iterated Bias & Polarization
• Black Box models

Tell me more: Counter-Polarization

Tell me why: Explanation Generation

Analysis Of Complex Vertebrate Genomes: Computational Challenges And Solutions, Jeramiah J. Smith

Commonwealth Computational Summit

No abstract provided.

Accurate And Scalable Query Over Large Rna‐Seq Experiments, Jinze Liu

Commonwealth Computational Summit

No abstract provided.

Resource Efficient Design Of Quantum Circuits For Quantum Algorithms, Himanshu Thapliyal

Commonwealth Computational Summit

No abstract provided.

Additional Data Via Autonomous Systems To Supplement Traditional Sparse Sources For Weather Forecasting And Atmospheric Science, Suzanne Weaver Smith

Commonwealth Computational Summit

No abstract provided.

Correct Model Selection In Multiple Regression Analyses Of Big Data, Katherine L. Thompson

Commonwealth Computational Summit

Goals:

• Improve statistical modeling in a variety of application areas
• Correctly identify the relationships present in data sets
• Understand the difficulty in choosing the correct statistical model in big data

Integrative Computational Modeling For Developing Means To Manipulate Biological Cells And For Solving Complex Engineering Problems, Yu Zhao, Guigen Zhang

Commonwealth Computational Summit

A brief discussion on reductive vs integrative investigation

A case study: how integrative computational modeling helps advance the understanding and application of dielectrophoresis (DEP) in various situations

Other applications in advancing the design and development of nanopore, medical devices, novel materials, actuation devices, and coupled spectroscopic techniques, etc.

Cloud‐Based Text Analytics Harvesting, Cleaning And Analyzing Corporate Earnings Conference Calls, Michael Chuancai Zhang, Vikram Gazula, Dan Stone, Hong Xie

Commonwealth Computational Summit

No abstract provided.

Grading Complex Tasks Through Crowdsourcing, Lingyu Lyu

Commonwealth Computational Summit

Crowdsourcing provides huge opportunities and scalability solutions for grading large scale tasks, such as MOOCs.

Reliability and quality of graders and crowdsourced data are challenging issues.

Workers might give random grades, which are spam; or provide biased grades, which need to be corrected.

The budget for hiring graders is limited, in many cases.

Parallelization Of A Three-Dimensional Full Multigrid Algorithm To Simulate Tumor Growth, Dylan Goodin, Chin F. Ng, Hermann B. Frieboes

Commonwealth Computational Summit

We present the performance gains of an openMP implementation of a fully adaptive nonlinear full multigrid (FMG) algorithm to simulate three-dimensional multispecies desmoplastic tumor growth on computer systems of varying processing capabilities. The FMG algorithm is applied to solve a recently published thermodynamic mixture model that uses a diffuse interface approach with fourth-order reaction-advection-diffusion PDEs (Cahn-Hilliard-type equations) that are coupled, nonlinear, and numerically stiff. The model includes multiple cell species and extracellular matrix (ECM), with adhesive and elastic energy contributions in chemical potential terms, as well as including blood and lymphatic vessels represented as continuous vasculatures. Advection-reaction-diffusion PDEs are employed …

The Effect Of Inlet Pulsations On Primary Atomization Of Liquid Jets, Kyle Windland, Prashant Khare

Commonwealth Computational Summit

Objectives

• Elucidate the physics underlying the primary atomization of liquid jets.
• Investigate the effect of inlet pulsations on the atomization process.
• Identify the reliability of numerical predictions using uncertainty quantification techniques (UQ) and sensitivity analyses.

Computational Materials Characterization, Discovery, And Design With High Performance Computing, Qunfei Zhou, Xiaotao Liu, Tyler Maxwell, Thomas John Balk, Matthew J. Beck

Commonwealth Computational Summit

No abstract provided.

New Explainable Active Learning Approach For Recommender Systems, Sami Khenissi, Behnoush Abdollahi, Wenlong Sun, Pegah Sagheb, Olfa Nasraoui

Commonwealth Computational Summit

Introduction and Motivations

• Recommender Systems are intelligent programs that analyze patterns between items and users to predict the user’s taste.

Objective

• Design an efficient Active Learning Strategy to increase the explainability and the accuracy of an “Explainable Matrix Factorization” model.

A Network Tomography Approach For Traffic Monitoring In Smart Cities, Ruoxi Zhang, Sara Newman, Marco Ortolani, Simone Silvestri

Commonwealth Computational Summit

Various urban planning and managing activities required by a Smart City are feasible because of traffic monitoring. As such, this project proposes a network tomography-based approach that can be applied to road networks to achieve a cost-efficient, flexible, and scalable monitor deployment. Due to the algebraic approach of network tomography, the selection of monitoring intersections can be solved through the use of matrices, with its rows representing paths between two intersections, and its columns representing links in the road network. Because the goal of the algorithm is to provide an inexpensive monitor set, this problem can be translated into a …

Bio-Inspired Disaster Response Networks, Vijay K. Shah, Simone Silvestri, Sajal K. Das, Satyaki Roy

Commonwealth Computational Summit

Large-scale natural disasters (e.g., Earthquake, Hurricane) –

• Three times as many disasters between 1980 and 2016 compared to 1940-1980. (EM-DAT – The International Disaster Database)
• Since 1990, 217 million people affected each year. (The New England Journal of Medicine)

Aftermath a disaster,

• Loss of human lives and property
• Lack of food, clean drinking water, shelter etc.
• Disruption of infrastructure networks (e.g. cellular towers) and other public infrastructures (e.g. power sources) – Our focus !

Formation Of Supermassive Black Holes In The Early Universe: High-Resolution Numerical Simulations Of Radiation Transfer Inside Collapsing Gas, Yang Luo, Kazem Ardaneh, Isaac Shlosman, Kentaro Nagamine, John Wise, Mitchell C. Begelman

Commonwealth Computational Summit

Observations of high-redshift quasars reveal that super massive black holes (SMBHs) with masses exceeding 10⁹ M_⊙ formed as early as redshift z ~ 7 [1,3,6]. This means that SMBHs have already formed ~700 million years after the Big Bang. How did such SMBHs could grow so quickly?

In this work, we use a modified and improved version of the blockstructured adaptive mesh refinement (AMR) code ENZO [2] to provide high spatial and temporal resolution for modeling the formation of SMBHs via direct collapse within dark matter (DM) halos at high redshifts. The radiation hydrodynamics equations are solved in …

Integrative Computational Modeling For Developing Means To Manipulate Biological Cells And For Solving Complex Engineering Problems, Yu Zhao, Guigen Zhang

Commonwealth Computational Summit

Computational modeling has become more widely used to guide the design of microfluidic devices for manipulating cells using Dielectrophoresis (DEP), and devise novel means for advancing the study of cellular science and engineering. Conventionally, cells are treated as volumeless points in the system, which allows study of the movement of groups of particles under the effect of field. However, this approach often neglects the distortion effect of particle on external field, as well as interactions among particles. Moreover, it ignores the complex inner structures of cell, which are the causes of distinctive cell behavior. To more accurately model the behavior …

Dynamic Load Balancing Based On Live Virtual Machine Migration, Manh Do, Michael Galloway

Commonwealth Computational Summit

Recently, cloud computing is a new trend emerging in computer technology with a huge demand from the clients, which leads to the consumption of a tremendous amount of energy. Load balancing is taken into account as a vital part of managing income demand, improving the cloud system's performance and reducing the energy cost. Live virtual machine migration is a technique to perform the dynamic load balancing algorithm. To optimize the cloud cluster, there are three issues to consider: First, how does the cloud cluster distribute the virtual machine (VM) requests from clients to all physical machine (PM) when each machine …

Cloud-Based Text Analytics: Harvesting, Cleaning And Analyzing Corporate Earnings Conference Calls, Michael Chuancai Zhang, Vikram Gazula, Dan Stone, Hong Xie

Commonwealth Computational Summit

Does management language cohesion in earnings conference calls matter to the capital market? As a part of the research on the above question, and taking advantage of the modern IT technologies, this project:

• harvested 115,882 earnings conference call transcripts from SeekingAlpha.com
• parsed and structured 89,988 transcripts using regular expressions in Stata
• analyzed 179,976 text files using Amazon Elastic Compute Cloud (Amazon EC2), which
• saved almost 2 years (675 days) of the project time

High-Fidelity Simulations Of Water Jet In Air Crossflow, Austin Johnston, Prashant Khare

Commonwealth Computational Summit

Objectives

• Investigate detailed physics underlying liquid jets in crossflow configurations applicable to various applications such as, gas-turbine, scramjet, and afterburner fuel injection.
• Develop models to predict the statistical behaviors of resulting droplets.

Adversarial Discriminative Domain Adaptation For Extracting Protein-Protein Interactions From Text, Anthony Rios, Ramakanth Kavuluru, Zhiyong Lu

Commonwealth Computational Summit

Relation extraction is the process of extracting structured information from unstructured text. Recently, neural networks (NNs) have produced state-of-art results in extracting protein-protein interactions (PPIs) from text. While multiple corpora have been created to extract PPIs from text, most methods have shown poor cross-corpora generalization. In other words, models trained on one dataset perform poorly on other datasets for the same task. In the case of PPI, the F1 has been shown to vary by as much as 30% between different datasets. In this work, we utilize adversarial discriminative domain adaptation (ADDA) to improve the generalization between the source and …

Imputing Trust Network Information In Nmf‐Based Recommendation Systems, Fatemah Alghamedy, Jun Zhang

Commonwealth Computational Summit

With the emergence of E‐commerce, recommendation system becomes a significant tool which can help both sellers and buyers. It helps sellers by increasing the profits and advertising items to customers. In addition, recommendation systems facilitate buyers to find items they are looking for easily.

In recommendation systems, the rating matrix R represents users' ratings for items. The rows in the rating matrix represent the users and the columns represent items. If particular user rates a particular item, then the value of the intersection of the user row and item column holds the rating value. The trust matrix T describes the …

Discovery Of Sex-Specific Regions In A Salamander Genome, Nataliya Y. Timoshevskaya, Melissa C. Keinath, Jeramiah J. Smith

Commonwealth Computational Summit

Biological Aspects:

Salamander (Ambystoma mexicanum) has a gigantic genome: ~32,000,000,000 bases (10X of size of human genome)

Sex is determined by a pair of morphologically identical chromosomes:

• ZZ in male
• ZW in female

Object:

• Find (if there are any) genomic differences between chromosomes W and Z

Workflow:

1. Sequencing and de novo assembly of the reference salamander genome
2. Alignment of short sequences from male and female genomes to the reference
3. Coverage analysis