Physical Sciences and Mathematics Commons^™

Increasing Impacts Of Extreme Droughts On Vegetation Productivity Under Climate Change, Chonggang Xu, Nate G. Mcdowell, Rosie A. Fisher, Liang Wei, Sanna Sevanto, Bradley O. Christoffersen, Engsheng Weng, Richard S. Middleton

School of Earth, Environmental, and Marine Sciences Faculty Publications and Presentations

Terrestrial gross primary production (GPP) is the basis of vegetation growth and food production globally1 and plays a critical role in regulating atmospheric CO2 through its impact on ecosystem carbon balance. Even though higher CO2 concentrations in future decades can increase GPP2, low soil water availability, heat stress and disturbances associated with droughts could reduce the benefits of such CO2 fertilization. Here we analysed outputs of 13 Earth system models to show an increasingly stronger impact on GPP by extreme droughts than by mild and moderate droughts over the twenty-first century. Due to a dramatic increase in …

Search For Gravitational-Wave Signals Associated With Gamma-Ray Bursts During The Second Observing Run Of Advanced Ligo And Advanced Virgo, B. P. Abbott, R. Abbott, Teviet Creighton, Mario C. Diaz, Soma Mukherjee, Volker Quetschke, Malik Rakhmanov, K. E. Ramirez, Satzhan Sitmukhambetov, Robert Stone, D. Tuyenbayev, W. H. Wang

Physics and Astronomy Faculty Publications and Presentations

We present the results of targeted searches for gravitational-wave transients associated with gamma-ray bursts during the second observing run of Advanced LIGO and Advanced Virgo, which took place from 2016 November to 2017 August. We have analyzed 98 gamma-ray bursts using an unmodeled search method that searches for generic transient gravitational waves and 42 with a modeled search method that targets compact-binary mergers as progenitors of short gamma-ray bursts. Both methods clearly detect the previously reported binary merger signal GW170817, with p-values of z ≤ 1. We estimate 0.07–1.80 joint detections with Fermi-GBM per year for the 2019–20 …

Individual Variation And Ecotypic Niches In Simulations Of The Impact Of Climatic Volatility, George P. Malanson, R. Justin Derose, Matthew F. Bekker

Wildland Resources Faculty Publications

Expectations of the impacts of climatic variation on species can depend on whether and how intraspecific variability is incorporated in models. Coefficients of variation from tree-ring records of Pinus albicaulis through time and across space were used to parameterize volatility and individuality, respectively. The records across sites were used to differentiate the average modes on an environmental gradient for Gaussian fitness of ecotypic niches, and to add further individual variation in mode and standard deviation of these functions in individual-based Monte Carlo simulations of reproduction and mortality with inheritance of individual variability. Ecotypic gamma and Shannon diversity decreased with volatility …

Can Florida's Springs Coast Provide A Potential Refuge For Calcifying Organisms? Evidence From Benthic Foraminifera, Kyle E. Amergian

USF Tampa Graduate Theses and Dissertations

Florida’s Springs Coast, located in the northeast Gulf of Mexico, includes an extensive system of salt marshes that discharge millions of liters of fresh water into coastal waters daily. The chemical properties of the spring waters include high alkalinity and high calcium concentrations due to the Paleogene limestone lithology of this region of Florida. Benthic foraminifers, which are recognized as ecologically important bioindicators, occur abundantly on the shallow shelf off the Springs Coast. Based on the prevalence of the benthic foraminifer Archaias angulatus in the seagrass beds along this shallow shelf, a previous study proposed that the Springs Coast provides …

Desertification Risk Analysis Based On Soils And Climate In Olive Cultivated Areas In Tulare County, California, Zehra Kavakli Karatas

USF Tampa Graduate Theses and Dissertations

Desertification is one of the most critical environmental problems caused by human activities and climate change. As a result of human activities, land degradation has been seen in many agricultural areas. Intense pressure on cultivated fields causes loss of soil fertility, which can then lead to desertification. Planting plant drought-resistant plants, such as olives, is one strategy for reducing desertification risk in cultivated areas. It is essential to find a way not only how to combat this process but also how to adapt or survive with desertification conditions. Defining desertification risks have a fundamental role in combating drought. The goal …

Measuring And Utilizing High-Dimensional Information Of Optical Fields, Ziyi Zhu

USF Tampa Graduate Theses and Dissertations

Currently, many areas of optical techniques including imaging, inspection and communication emphasize the utilization of the high-dimensional information encoded in optical fields. There is also a requirement for novel measurement techniques to extract this high-dimensional information with high-speed and accuracy. We firstly introduce a scan-free direct measurement technique that is capable of simultaneously characterizing the amplitude and phase of a coherent scalar optical field. Our direct measurement approach is constituted of a weak polarization perturbation which is followed by the recording of a polarization-resolving imaging process. The weak perturbation rotates the linear polarization on the spatial frequency domain of the …

Fractional Random Weighted Bootstrapping For Classification On Imbalanced Data With Ensemble Decision Tree Methods, Sean Charles Carter

USF Tampa Graduate Theses and Dissertations

Ensemble methods are commonly used for building predictive models for classification. Models that are unstable to perturbations in the training set, such as the decision tree, often see considerable reductions in error when grouped, using bootstrapped resamples of the training data to train many models. The non-parametric bootstrap, however, has limited efficacy when used on severely imbalanced data, especially when the number of observations of one or more classes is exceptionally small. We explore the fractional random weighted bootstrap, which randomly assigns fractional weights to observations, as an alternative resampling pro cedure in training machine learning ensembles, particularly decision tree …

Urban-Rural Surface Temperature Deviation And Intra-Urban Variations Contained By An Urban Growth Boundary, Kevan B. Moffett, Yasuyo Makido, Vivek Shandas

Urban Studies and Planning Faculty Publications and Presentations

The urban heat island (UHI) concept describes heat trapping that elevates urban temperatures relative to rural temperatures, at least in temperate/humid regions. In drylands, urban irrigation can instead produce an urban cool island (UCI) effect. However, the UHI/UCI characterization suffers from uncertainty in choosing representative urban/rural endmembers, an artificial dichotomy between UHIs and UCIs, and lack of consistent terminology for other patterns of thermal variation at nested scales. We use the case of a historically well-enforced urban growth boundary (UGB) around Portland (Oregon, USA): to explore the representativeness of the surface temperature UHI (SUHI) as derived from Moderate Resolution Imaging …

Learning-Guided Network Fuzzing For Testing Cyber-Physical System Defences, Yuqi Chen, Chris Poskitt, Jun Sun, Sridhar Adepu, Fan Zhang

Research Collection School Of Computing and Information Systems

The threat of attack faced by cyber-physical systems (CPSs), especially when they play a critical role in automating public infrastructure, has motivated research into a wide variety of attack defence mechanisms. Assessing their effectiveness is challenging, however, as realistic sets of attacks to test them against are not always available. In this paper, we propose smart fuzzing, an automated, machine learning guided technique for systematically finding 'test suites' of CPS network attacks, without requiring any knowledge of the system's control programs or physical processes. Our approach uses predictive machine learning models and metaheuristic search algorithms to guide the fuzzing of …

Data Security Issues In Deep Learning: Attacks, Countermeasures, And Opportunities, Guowen Xu, Hongwei Li, Hao Ren, Kan Yang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Benefiting from the advancement of algorithms in massive data and powerful computing resources, deep learning has been explored in a wide variety of fields and produced unparalleled performance results. It plays a vital role in daily applications and is also subtly changing the rules, habits, and behaviors of society. However, inevitably, data-based learning strategies are bound to cause potential security and privacy threats, and arouse public as well as government concerns about its promotion to the real world. In this article, we mainly focus on data security issues in deep learning. We first investigate the potential threats of deep learning …

Automated Theme Search In Ico Whitepapers, Chuanjie Fu, Andrew Koh, Paul Griffin

Research Collection School Of Computing and Information Systems

The authors explore how topic modeling can be used to automate the categorization of initial coin offerings (ICOs) into different topics (e.g., finance, media, information, professional services, health and social, natural resources) based solely on the content within the whitepapers. This tool has been developed by fitting a latent Dirichlet allocation (LDA) model to the text extracted from the ICO whitepapers. After evaluating the automated categorization of whitepapers using statistical and human judgment methods, it is determined that there is enough evidence to conclude that the LDA model appropriately categorizes the ICO whitepapers. The results from a two-population proportion test …

Stressmon: Scalable Detection Of Perceived Stress And Depression Using Passive Sensing Of Changes In Work Routines And Group Interactions, Nur Camellia Binte Zakaria, Rajesh Balan, Youngki Lee

Research Collection School Of Computing and Information Systems

Stress and depression are a common affliction in all walks of life. When left unmanaged, stress can inhibit productivity or cause depression. Depression can occur independently of stress. There has been a sharp rise in mobile health initiatives to monitor stress and depression. However, these initiatives usually require users to install dedicated apps or multiple sensors, making such solutions hard to scale. Moreover, they emphasise sensing individual factors and overlook social interactions, which plays a significant role in influencing stress and depression while being a part of a social system. We present StressMon, a stress and depression detection system that …

Map-Coverage: A Novel Coverage Criterion For Testing Thread-Safe Classes, Zan Wang, Yingquan Zhao, Shuang Liu, Jun Sun, Xiang Chen, Huarui Lin

Research Collection School Of Computing and Information Systems

Concurrent programs must be thoroughly tested, as concurrency bugs are notoriously hard to detect. Code coverage criteria can be used to quantify the richness of a test suite (e.g., whether a program has been tested sufficiently) or provide practical guidelines on test case generation (e.g., as objective functions used in program fuzzing engines). Traditional code coverage criteria are, however, designed for sequential programs and thus ineffective for concurrent programs. In this work, we introduce a novel code coverage criterion for testing thread-safe classes called MAP-coverage (short for memory-access patterns). The motivation is that concurrency bugs are often correlated with certain …

Electroosmotic Flow Of Viscoelastic Fluid In A Nanochannel Connecting Two Reservoirs, Lanju Mei, Shizhi Qian

Mechanical & Aerospace Engineering Faculty Publications

Electroosmotic flow (EOF) of viscoelastic fluid with Linear Phan-Thien–Tanner (LPTT) constitutive model in a nanochannel connecting two reservoirs is numerically studied. For the first time, the influence of viscoelasticity on the EOF and the ionic conductance in the micro-nanofluidic interconnect system, with consideration of the electrical double layers (EDLs), is investigated. Regardless of the bulk salt concentration, significant enhancement of the flow rate is observed for viscoelastic fluid compared to the Newtonian fluid, due to the shear thinning effect. An increase in the ionic conductance of the nanochannel occurs for the viscoelastic fluid. The enhancement of the ionic conductance is …

2019 Chesapeake Bay Dead Zone Report, Virginia Institute Of Marine Science, Anchor Qea

Reports

The “Dead Zone” of the Chesapeake Bay refers to a volume of bottom water that is characterized by dissolved oxygen concentrations less than 2 mg/L, which is too low for aquatic organisms such as fish and blue crabs to thrive. The Chesapeake Bay experiences such “hypoxic”conditions every year, with the severity varying from year to year, depending on nutrient and freshwater inputs, wind, and temperature. Multiple metrics are used to relate the severity of hypoxia between different years:

• Maximum Daily Hypoxic Volume (km3): The maximum volume of Chesapeake Bay water experiencing hypoxic conditions on any given day

• Average Summer Hypoxic …

Shellnet: Efficient Point Cloud Convolutional Neural Networks Using Concentric Shells Statistics, Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung

Research Collection School Of Computing and Information Systems

Deep learning with 3D data has progressed significantly since the introduction of convolutional neural networks that can handle point order ambiguity in point cloud data. While being able to achieve good accuracies in various scene understanding tasks, previous methods often have low training speed and complex network architecture. In this paper, we address these problems by proposing an efficient end-to-end permutation invariant convolution for point cloud deep learning. Our simple yet effective convolution operator named ShellConv uses statistics from concentric spherical shells to define representative features and resolve the point order ambiguity, allowing traditional convolution to perform on such features. …

Low-Resource Name Tagging Learned With Weakly Labeled Data, Yixin Cao, Zikun Hu, Tat-Seng Chua, Zhiyuan Liu, Heng Ji

Research Collection School Of Computing and Information Systems

Name tagging in low-resource languages or domains suffers from inadequate training data. Existing work heavily relies on additional information, while leaving those noisy annotations unexplored that extensively exist on the web. In this paper, we propose a novel neural model for name tagging solely based on weakly labeled (WL) data, so that it can be applied in any low-resource settings. To take the best advantage of all WL sentences, we split them into high-quality and noisy portions for two modules, respectively: (1) a classification module focusing on the large portion of noisy data can efficiently and robustly pretrain the tag …

Visualizing The Invisible: Occluded Vehicle Segmentation And Recovery, Xiaosheng Yan, Feigege Wang, Wenxi Liu, Yuanlong Yu, Shengfeng He, Jia Pan

Research Collection School Of Computing and Information Systems

In this paper, we propose a novel iterative multi-task framework to complete the segmentation mask of an occluded vehicle and recover the appearance of its invisible parts. In particular, firstly, to improve the quality of the segmentation completion, we present two coupled discriminators that introduce an auxiliary 3D model pool for sampling authentic silhouettes as adversarial samples. In addition, we propose a two-path structure with a shared network to enhance the appearance recovery capability. By iteratively performing the segmentation completion and the appearance recovery, the results will be progressively refined. To evaluate our method, we present a dataset, Occluded Vehicle …

A Quantitative Analysis Framework For Recurrent Neural Network, Xiaoning Du, Xiaofei Xie, Yi Li, Lei Ma, Yang Liu, Jianjun Zhao

Research Collection School Of Computing and Information Systems

Recurrent neural network (RNN) has achieved great success in processing sequential inputs for applications such as automatic speech recognition, natural language processing and machine translation. However, quality and reliability issues of RNNs make them vulnerable to adversarial attacks and hinder their deployment in real-world applications. In this paper, we propose a quantitative analysis framework — DeepStellar— to pave the way for effective quality and security analysis of software systems powered by RNNs. DeepStellar is generic to handle various RNN architectures, including LSTM and GRU, scalable to work on industrial-grade RNN models, and extensible to develop customized analyzers and tools. We …

Predicting Audience Engagement Across Social Media Platforms In The News Domain, Kholoud Khalil Aldous, Jisun An, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

We analyze cross-platform factors for posts on both single and multiple social media platforms for numerous news outlets to better predict audience engagement, precisely the number of likes and comments. We collect 676,779 social media posts from 53 news outlets during eight months on four social media platforms (Facebook, Instagram, Twitter, and YouTube), along with the associated comments (more than 31 million) and the number of likes (more than 840 million). We develop a framework for predicting the audience engagement based on both linguistic features of the post and social media platform factors. Among other findings, results show that content …

Choosing Protection: User Investments In Security Measures For Cyber Risk Management, Yoav Ben Yaakov, Xinrun Wang, Joachim Meyer, Bo An

Research Collection School Of Computing and Information Systems

Firewalls, Intrusion Detection Systems (IDS), and cyber-insurance are widely used to protect against cyber-attacks and their consequences. The optimal investment in each of these security measures depends on the likelihood of threats and the severity of the damage they cause, on the user’s ability to distinguish between malicious and non-malicious content, and on the properties of the different security measures and their costs. We present a model of the optimal investment in the security measures, given that the effectiveness of each measure depends partly on the performance of the others. We also conducted an online experiment in which participants classified …

Smrtfridge: Iot-Based, User Interaction-Driven Food Item & Quantity Sensing, Amit Sharma, Archan Misra, Vengateswaran Subramaniam, Youngki Lee

Research Collection School Of Computing and Information Systems

We present SmrtFridge, a consumer-grade smart fridge prototype that demonstrates two key capabilities: (a) identify the individual food items that users place in or remove from a fridge, and (b) estimate the residual quantity of food items inside a refrigerated container (opaque or transparent). Notably, both of these inferences are performed unobtrusively, without requiring any explicit user action or tagging of food objects. To achieve these capabilities, SmrtFridge uses a novel interaction-driven, multi-modal sensing pipeline, where Infrared (IR) and RGB video sensing, triggered whenever a user interacts naturally with the fridge, is used to extract a foreground visual image of …

Estimating Glycemic Impact Of Cooking Recipes Via Online Crowdsourcing And Machine Learning, Helena Lee, Palakorn Achananuparp, Yue Liu, Ee-Peng Lim, Lav R. Varshney

Research Collection School Of Computing and Information Systems

Consumption of diets with low glycemic impact is highly recommended for diabetics and pre-diabetics as it helps maintain their blood glucose levels. However, laboratory analysis of dietary glycemic potency is time-consuming and expensive. In this paper, we explore a data-driven approach utilizing online crowdsourcing and machine learning to estimate the glycemic impact of cooking recipes. We show that a commonly used healthiness metric may not always be effective in determining recipes suitable for diabetics, thus emphasizing the importance of the glycemic-impact estimation task. Our best classification model, trained on nutritional and crowdsourced data obtained from Amazon Mechanical Turk (AMT), can …

Statistical Log Differencing, Lingfeng Bao, Nimrod Busany, David Lo, Shahar Maoz

Research Collection School Of Computing and Information Systems

Recent works have considered the problem of log differencing: given two or more system’s execution logs, output a model of their differences. Log differencing has potential applications in software evolution, testing, and security. In this paper we present statistical log differencing, which accounts for frequencies of behaviors found in the logs. We present two algorithms, s2KDiff for differencing two logs, and snKDiff, for differencing of many logs at once, both presenting their results over a single inferred model. A unique aspect of our algorithms is their use of statistical hypothesis testing: we let the engineer control the sensitivity of the …

Closing The Oxygen Mass Balance In Shallow Coastal Ecosystems, Matthew H. Long, Jennie E. Rheuban, Daniel C. Mccorkle, David J. Burdige, Richard C. Zimmerman

OES Faculty Publications

The oxygen concentration in marine ecosystems is influenced by production and consumption in the water column and fluxes across both the atmosphere-water and benthic-water boundaries. Each of these fluxes has the potential to be significant in shallow ecosystems due to high fluxes and low water volumes. This study evaluated the contributions of these three fluxes to the oxygen budget in two contrasting ecosystems, a Zostera marina (eelgrass) meadow in Virginia, U.S.A., and a coral reef in Bermuda. Benthic oxygen fluxes were evaluated by eddy covariance. Water column oxygen production and consumption were measured using an automated water incubation system. Atmosphere-water …

Seaflow Data V1, High-Resolution Abundance, Size And Biomass Of Small Phytoplankton In The North Pacific, François Ribalet, Chris Berthiaume, Annette Hynes, Jarred Swalwell, Michael Carlson, Sophie Clayton, Gwenn Hennon, Camille Poirier, Eric Shimabukuro, Angelicque White, E. Virginia Armhurst

OES Faculty Publications

SeaFlow is an underway flow cytometer that provides continuous shipboard observations of the abundance and optical properties of small phytoplankton (μm in equivalent spherical diameter, ESD). Here we present data sets consisting of SeaFlow-based cell abundance, forward light scatter, and pigment fluorescence of individual cells, as well as derived estimates of ESD and cellular carbon content of picophytoplankton, which includes the cyanobacteria Prochlorococcus, Synechococcus and small-sized Crocosphaera (μm ESD), and picophytoplankton and nanophytoplankton (2–5 μm ESD). Data were collected in surface waters (≈5 m depth) from 27 oceanographic cruises carried out in the Northeast Pacific Ocean between 2010 and 2018. …

Electronic Field Trips For Science Engagement: The Streaming Science Model, Jamie Loizzo, Mary J. Harner, Deborah J. Weitzenkamp, Kevin Kent

Journal of Applied Communications

While institutions of higher education work to engage PK-12 youth in STEM (science, technology, engineering, and mathematics) concepts and careers via in-person programming, PK-12 teachers and students face many logistical and access constraints for physically traveling to sites off of school grounds during the school day. Throughout the years, electronic field trips (EFTs) have offered a digital way for schools to engage in meaningful ways with museums, parks, laboratories, and field research sites. In order for EFTs to be effective, they should be cost effective and created collaboratively with teachers, students, subject matter experts, and instructional design and communication professionals. …

Salt Tolerance Of Sego Supremetm Plants, Asmita Paudel, Ji Jhong Chen, Youping Sun, Yuxiang Wang, Richard M. Anderson

Plants, Soils, and Climate Faculty Publications

Sego Supreme^TM is a designated plant breeding and introduction program at the Utah State University Botanical Center and the Center for Water Efficient Landscaping. This plant selection program introduces native and adapted plants to the arid West for aesthetic landscaping and water conservation. The plants are evaluated for characteristics such as color, flowering, ease of propagation, market demand, disease/pest resistance, and drought tolerance. However, salt tolerance has not been considered during the evaluation processes. Four Sego Supreme^TM plants [Aquilegia barnebyi (oil shale columbine), Clematis fruticosa (Mongolian gold clematis), Epilobium septentrionale (northern willowherb), and Tetraneuris acaulis var. arizonica …

Student Perceptions Of Learning Introductory Mathematics In An Online Environment In Higher Education, Jamie Lynn Brooks

Doctoral Dissertations and Projects

The purpose of this transcendental phenomenological study was to describe the essence of student perception of learning introductory mathematics courses in an online environment at the college level. The central research question was, “What are the lived experiences of students who have completed introductory college mathematics courses in the online learning environment?” The phenomenon described was that of the beliefs and attitudes of the students who participated in introductory mathematics courses on the college level. The ideas explored were if students believe they learn effectively in this environment and how they believe they can best learn. Student beliefs and attitudes …

Longitudinal Bunch Profile Diagnostic For Magnetized Electron Beams, Mark Stefani, Fay Hannon

Electrical & Computer Engineering Faculty Publications

The study of magnetized electron beam has become a high priority for its use in ion beam cooling as part of electron ion colliders and the potential of easily forming flat beams with a large aspect ratio. In this paper, a new diagnostic is described with the purpose of studying longitudinal and transverse magnetized beam properties. The device is a modification to a typical pepper-pot. Specifically, this 1D pepper-pot was designed for use with a transverse deflecting cavity for longitudinal bunch profile measurements of magnetized beams.