Physical Sciences and Mathematics Commons^™

Predicting Forage Provision Of Grasslands Across Climate Zones By Hyperspectral Measurements, F. A. Männer, J. Muro, J. Ferner, S. Schmidtlein, A. Linstädter

IGC Proceedings (1997-2023)

The potential of grasslands’ fodder production is a crucial management measure, while its quantification is still laborious and costly. Remote sensing technologies, such as hyperspectral field measurements, enable fast and non-destructive estimation. However, such methods are still limited in transferability to other locations or climatic conditions. With this study, we aim to predict forage nutritive value, quantity, and energy yield from hyperspectral canopy reflections of grasslands across three climate zones. We took hyperspectral measurements with a field spectrometer from grassland canopies in temperate, tropical and semi-arid grasslands, and analyzed corresponding biomass samples for their quantity (BM), metabolizable energy content (ME) …

Molecular Understanding And Design Of Deep Eutectic Solvents And Proteins Using Computer Simulations And Machine Learning, Usman Lame Abbas

Theses and Dissertations--Chemical and Materials Engineering

Hydrophobic deep eutectic solvents (DESs) have emerged as excellent extractants. A major challenge is the lack of an efficient tool to discover DES candidates. Currently, the search relies heavily on the researchers’ intuition or a trial-and-error process, which leads to a low success rate or bypassing of promising candidates. DES performance depends on the heterogeneous hydrogen bond environment formed by multiple hydrogen bond donors and acceptors. Understanding this heterogeneous hydrogen bond environment can help develop principles for designing high performance DESs for extraction and other separation applications. This work investigates the structure and dynamics of hydrogen bonds in hydrophobic DESs …

Discriminating Stay-Green Grasses Using Hyperspectral Imaging And Chemometrics, J. Taylor, B. Moore, J. J. Rowland, H. Thomas, H. J. Ougham

IGC Proceedings (1997-2023)

Screening of plant collections for traits can be expensive, in terms of the number of plants to be screened, the duration of the plant lifecycle and the required observations. This study describes the application of a non-invasive method, hyperspectral imaging, combined with multivariate analysis, to distinguish between homozygous wild-type (YY) Lolium multiflorum and Lolium multiflorum F2 back cross plants heterozygous for y, a recessive Festuca pratensis stay-green gene (Thomas et al., 1997).

The Basil Technique: Bias Adaptive Statistical Inference Learning Agents For Learning From Human Feedback, Jonathan Indigo Watson

Theses and Dissertations--Computer Science

We introduce a novel approach for learning behaviors using human-provided feedback that is subject to systematic bias. Our method, known as BASIL, models the feedback signal as a combination of a heuristic evaluation of an action's utility and a probabilistically-drawn bias value, characterized by unknown parameters. We present both the general framework for our technique and specific algorithms for biases drawn from a normal distribution. We evaluate our approach across various environments and tasks, comparing it to interactive and non-interactive machine learning methods, including deep learning techniques, using human trainers and a synthetic oracle with feedback distorted to varying degrees. …

Applications Of Digital Terrain Modeling To Address Problems In Geomorphology And Engineering Geology, Sarah Johnson

Theses and Dissertations--Earth and Environmental Sciences

This dissertation uses digital terrain modeling and computational methods to yield insight into three topics: 1) evaluating the influence of glacial topography on fluvial sediment transport in the Teton Range, WY, 2) integrating regional airborne lidar, UAV lidar, and structure from motion photogrammetry to characterize decadal-scale movement of slow-moving landslides in northern Kentucky, and 3) applying machine learning methods to surficial geologic mapping.

The role of topography as a boundary condition that controls the efficiency of fluvial erosion in the Teton Range, Wyoming, was investigated by using existing lidar data to delineate surficial geologic units, geometrically reconstruct the depth to …

Historical And Forecasted Kentucky Specific Slope Stability Analyses Using Remotely Retrieved Hydrologic And Geomorphologic Data, Daniel M. Francis

Theses and Dissertations--Civil Engineering

Hazard analyses of rainfall-induced landslides have typically been observed to experience a lack of inclusion of measurements of soil moisture within a given soil layer at a site of interest. Soil moisture is a hydromechanical variable capable of both strength gains and reductions within soil systems. However, in situ monitoring of soil moisture at every site of interest is an unfeasible goal. Therefore, spatiotemporal estimates of soil moisture that are representative of in-situ conditions are required for use in subsequent landslide hazard analyses.

This study brings together various techniques for the acquisition, modeling, and forecasting of spatiotemporal retrievals of soil …

Assessing Machine Learning Utility In Predicting Hydrologic And Nitrate Dynamics In Karst Agroecosystems, Timothy Mcgill

Theses and Dissertations--Biosystems and Agricultural Engineering

Seasonal hypoxia in the Gulf of Mexico and harmful algal blooms experienced in many inland freshwater bodies is partially driven due to excessive nitrogen loading seen from agricultural watersheds. Within the Mississippi/Atchafalaya River Basin, many areas are underlain with karst features, and efforts to reduce nitrogen contributions from these areas have had varying success, due to lacking a complete understanding of nutrient dynamics in karst agricultural systems. To improve the understanding of nitrogen cycling in these systems, 35 months of high resolution in situ water quality and atmospheric data were collected and fed into a two-hidden layer extreme learning machine …

Modeling Of Groundwater Potential Using Cloud Computing Platform: A Case Study From Nineveh Plain, Northern Iraq, Ali Za. Al-Ozeer, Alaa M. Al-Abadi, Tariq Abed Hussain, Alan E. Fryar, Biswajeet Pradhan, Abdullah Alamri, Khairul Nizam Abdul Maulud

Earth and Environmental Sciences Faculty Publications

Knowledge of the groundwater potential, especially in an arid region, can play a major role in planning the sustainable management of groundwater resources. In this study, nine machine learning (ML) algorithms—namely, Artificial Neural Network (ANN), Decision Jungle (DJ), Averaged Perceptron (AP), Bayes Point Machine (BPM), Decision Forest (DF), Locally-Deep Support Vector Machine (LD-SVM), Boosted Decision Tree (BDT), Logistic Regression (LG), and Support Vector Machine (SVM)—were run on the Microsoft Azure cloud computing platform to model the groundwater potential. We investigated the relationship between 512 operating boreholes with a specified specific capacity and 14 groundwater-influencing occurrence factors. The unconfined aquifer in …

Algebraic Graph-Assisted Bidirectional Transformers For Molecular Property Prediction, Dong Chen, Kaifu Gao, Duc Duy Nguyen, Xin Chen, Yi Jiang, Guo-Wei Wei, Feng Pan

Mathematics Faculty Publications

The ability of molecular property prediction is of great significance to drug discovery, human health, and environmental protection. Despite considerable efforts, quantitative prediction of various molecular properties remains a challenge. Although some machine learning models, such as bidirectional encoder from transformer, can incorporate massive unlabeled molecular data into molecular representations via a self-supervised learning strategy, it neglects three-dimensional (3D) stereochemical information. Algebraic graph, specifically, element-specific multiscale weighted colored algebraic graph, embeds complementary 3D molecular information into graph invariants. We propose an algebraic graph-assisted bidirectional transformer (AGBT) framework by fusing representations generated by algebraic graph and bidirectional transformer, as well as …

Revisiting Absolute Pose Regression, Hunter Blanton

Theses and Dissertations--Computer Science

Images provide direct evidence for the position and orientation of the camera in space, known as camera pose. Traditionally, the problem of estimating the camera pose requires reference data for determining image correspondence and leveraging geometric relationships between features in the image. Recent advances in deep learning have led to a new class of methods that regress the pose directly from a single image.

This thesis proposes methods for absolute camera pose regression. Absolute pose regression estimates the pose of a camera from a single image as the output of a fixed computation pipeline. These methods have many practical benefits …

Machine Learning And Bioinformatic Insights Into Key Enzymes For A Bio-Based Circular Economy, Japheth E. Gado

Theses and Dissertations--Chemical and Materials Engineering

The world is presently faced with a sustainability crisis; it is becoming increasingly difficult to meet the energy and material needs of a growing global population without depleting and polluting our planet. Greenhouse gases released from the continuous combustion of fossil fuels engender accelerated climate change, and plastic waste accumulates in the environment. There is need for a circular economy, where energy and materials are renewably derived from waste items, rather than by consuming limited resources. Deconstruction of the recalcitrant linkages in natural and synthetic polymers is crucial for a circular economy, as deconstructed monomers can be used to manufacture …

A New Efficient Method To Detect Genetic Interactions For Lung Cancer Gwas, Jennifer Luyapan, Xuemei Ji, Siting Li, Xiangjun Xiao, Dakai Zhu, Eric J. Duell, David C. Christiani, Matthew B. Schabath, Susanne M. Arnold, Shanbeh Zienolddiny, Hans Brunnström, Olle Melander, Mark D. Thornquist, Todd A. Mackenzie, Christopher I. Amos, Jiang Gui

Markey Cancer Center Faculty Publications

BACKGROUND: Genome-wide association studies (GWAS) have proven successful in predicting genetic risk of disease using single-locus models; however, identifying single nucleotide polymorphism (SNP) interactions at the genome-wide scale is limited due to computational and statistical challenges. We addressed the computational burden encountered when detecting SNP interactions for survival analysis, such as age of disease-onset. To confront this problem, we developed a novel algorithm, called the Efficient Survival Multifactor Dimensionality Reduction (ES-MDR) method, which used Martingale Residuals as the outcome parameter to estimate survival outcomes, and implemented the Quantitative Multifactor Dimensionality Reduction method to identify significant interactions associated with age of …

Integrated Multiparametric Radiomics And Informatics System For Characterizing Breast Tumor Characteristics With The Oncotypedx Gene Assay, Michael A. Jacobs, Christopher B. Umbricht, Vishwa S. Parekh, Riham H. El Khouli, Leslie Cope, Katarzyna J. Macura, Susan Harvey, Antonio C. Wolff

Radiology Faculty Publications

Optimal use of multiparametric magnetic resonance imaging (mpMRI) can identify key MRI parameters and provide unique tissue signatures defining phenotypes of breast cancer. We have developed and implemented a new machine-learning informatic system, termed Informatics Radiomics Integration System (IRIS) that integrates clinical variables, derived from imaging and electronic medical health records (EHR) with multiparametric radiomics (mpRad) for identifying potential risk of local or systemic recurrence in breast cancer patients. We tested the model in patients (n = 80) who had Estrogen Receptor positive disease and underwent OncotypeDX gene testing, radiomic analysis, and breast mpMRI. The IRIS method was trained …

Harnessing Artificial Intelligence Capabilities To Improve Cybersecurity, Sherali Zeadally, Erwin Adi, Zubair Baig, Imran A. Khan

Information Science Faculty Publications

Cybersecurity is a fast-evolving discipline that is always in the news over the last decade, as the number of threats rises and cybercriminals constantly endeavor to stay a step ahead of law enforcement. Over the years, although the original motives for carrying out cyberattacks largely remain unchanged, cybercriminals have become increasingly sophisticated with their techniques. Traditional cybersecurity solutions are becoming inadequate at detecting and mitigating emerging cyberattacks. Advances in cryptographic and Artificial Intelligence (AI) techniques (in particular, machine learning and deep learning) show promise in enabling cybersecurity experts to counter the ever-evolving threat posed by adversaries. Here, we explore AI's …

Text Mining Methods For Analyzing Online Health Information And Communication, Sifei Han

Theses and Dissertations--Computer Science

The Internet provides an alternative way to share health information. Specifically, social network systems such as Twitter, Facebook, Reddit, and disease specific online support forums are increasingly being used to share information on health related topics. This could be in the form of personal health information disclosure to seek suggestions or answering other patients' questions based on their history. This social media uptake gives a new angle to improve the current health communication landscape with consumer generated content from social platforms. With these online modes of communication, health providers can offer more immediate support to the people seeking advice. Non-profit …

Learning To Map The Visual And Auditory World, Tawfiq Salem

Theses and Dissertations--Computer Science

The appearance of the world varies dramatically not only from place to place but also from hour to hour and month to month. Billions of images that capture this complex relationship are uploaded to social-media websites every day and often are associated with precise time and location metadata. This rich source of data can be beneficial to improve our understanding of the globe. In this work, we propose a general framework that uses these publicly available images for constructing dense maps of different ground-level attributes from overhead imagery. In particular, we use well-defined probabilistic models and a weakly-supervised, multi-task training …

Beam-Target Helicity Asymmetry E In K0Λ And K0Σ0 Photoproduction On The Neutron, D. H. Ho, R. A. Schumacher, A. D’Angelo, A. Deur, J. Fleming, C. Hanretty, T. Kageya, F. J. Klein, E. Klempt, M. M. Lowry, H. Lu, V. A. Nikonov, P. Peng, A. M. Sandorfi, A. V. Sarantsev, I. I. Strakovsky, N. K. Walford, X. Wei, R. L. Workman, K. P. Adhikari, S. Adhikari, D. Adikaram, Z. Akbar, J. Ball, L. Barion, M. Bashkanov, C. D. Bass, M. Battaglieri, I. Bedlinskiy, A. S. Biselli, Wesley P. Gohn

Physics and Astronomy Faculty Publications

We report the first measurements of the E beam-target helicity asymmetry for the γ^→ n^→ → K⁰Λ and K⁰Σ⁰ channels in the energy range 1.70 ≤ W ≤ 2.34 GeV. The CLAS system at Jefferson Lab uses a circularly polarized photon beam and a target consisting of longitudinally polarized solid molecular hydrogen deuteride with low background contamination for the measurements. The multivariate analysis method boosted decision trees is used to isolate the reactions of interest. Comparisons with predictions from the KaonMAID, SAID, and Bonn-Gatchina models are presented. These results will help separate the …

Application Of Acoustic Emission And Machine Learning To Detect Codling Moth Infested Apples, Mengxing Li, Nader Ekramirad, Ahmed Rady, Akinbode A. Adedeji

Biosystems and Agricultural Engineering Faculty Publications

Incidence of codling moth (CM) (Cydia pomonella L.) infestation in apples has been a major concern in North America for decades. CM larvae bore deep into the fruit, making it unmarketable. An effective noninvasive method to detect larvae-infested apples is necessary to ensure that apples are CM-free in post-harvest processing. In this study, a novel approach using an acoustic emission (AE) system and subsequent machine learning methods was applied to classify larvae-infested apples from intact apples. 'GoldRush‘ apples were infested with CM neonates and stored at the same conditions as intact apples. The AE system was used to collect …

Leveraging Overhead Imagery For Localization, Mapping, And Understanding, Scott Workman

Theses and Dissertations--Computer Science

Ground-level and overhead images provide complementary viewpoints of the world. This thesis proposes methods which leverage dense overhead imagery, in addition to sparsely distributed ground-level imagery, to advance traditional computer vision problems, such as ground-level image localization and fine-grained urban mapping. Our work focuses on three primary research areas: learning a joint feature representation between ground-level and overhead imagery to enable direct comparison for the task of image geolocalization, incorporating unlabeled overhead images by inferring labels from nearby ground-level images to improve image-driven mapping, and fusing ground-level imagery with overhead imagery to enhance understanding. The ultimate contribution of this thesis …

Recurrent Neural Networks And Their Applications To Rna Secondary Structure Inference, Devin Willmott

Theses and Dissertations--Mathematics

Recurrent neural networks (RNNs) are state of the art sequential machine learning tools, but have difficulty learning sequences with long-range dependencies due to the exponential growth or decay of gradients backpropagated through the RNN. Some methods overcome this problem by modifying the standard RNN architecure to force the recurrent weight matrix W to remain orthogonal throughout training. The first half of this thesis presents a novel orthogonal RNN architecture that enforces orthogonality of W by parametrizing with a skew-symmetric matrix via the Cayley transform. We present rules for backpropagation through the Cayley transform, show how to deal with the Cayley …

Evolution Of Bias In Human And Machine Learning Algorithm Interaction, Wenlong Sun, Olfa Nasraoui, Patrick Shafto

Commonwealth Computational Summit

Human algorithm interaction:

• people are now affected by the output of all types of machine learning algorithms.
• social media, blogs, social networks, and other services and applications.

Motivation:

• ML algorithm relied on reliable labels from experts to build prediction.
• However, ML algorithm started to receive data from the more general population.
• The interaction leads to biased result which is caused by ingesting unchecked information from general population, such as biased samples and biased labels.

Identification Of Prognostic Genes And Gene Sets For Early-Stage Non-Small Cell Lung Cancer Using Bi-Level Selection Methods, Suyan Tian, Chi Wang, Howard H. Chang, Jianguo Sun

Biostatistics Faculty Publications

In contrast to feature selection and gene set analysis, bi-level selection is a process of selecting not only important gene sets but also important genes within those gene sets. Depending on the order of selections, a bi-level selection method can be classified into three categories – forward selection, which first selects relevant gene sets followed by the selection of relevant individual genes; backward selection which takes the reversed order; and simultaneous selection, which performs the two tasks simultaneously usually with the aids of a penalized regression model. To test the existence of subtype-specific prognostic genes for non-small cell lung cancer …

Singular Value Computation And Subspace Clustering, Qiao Liang

Theses and Dissertations--Mathematics

In this dissertation we discuss two problems. In the first part, we consider the problem of computing a few extreme eigenvalues of a symmetric definite generalized eigenvalue problem or a few extreme singular values of a large and sparse matrix. The standard method of choice of computing a few extreme eigenvalues of a large symmetric matrix is the Lanczos or the implicitly restarted Lanczos method. These methods usually employ a shift-and-invert transformation to accelerate the speed of convergence, which is not practical for truly large problems. With this in mind, Golub and Ye proposes an inverse-free preconditioned Krylov subspace method, …