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Articles 211 - 240 of 5947

Full-Text Articles in Physical Sciences and Mathematics

Nitrogen Fixation At The Mid-Atlantic Bight Shelfbreak And Transport Of Newly Fixed Nitrogen To The Slope Sea, C. R. Selden, M. R. Mulholland, K. E. Crider, S. Clayton, A. Macías-Tapia, P. Bernhardt, D. J. Mcgillicuddy Jr., W. G. Zhang, P. D. Chappell Jan 2024

Nitrogen Fixation At The Mid-Atlantic Bight Shelfbreak And Transport Of Newly Fixed Nitrogen To The Slope Sea, C. R. Selden, M. R. Mulholland, K. E. Crider, S. Clayton, A. Macías-Tapia, P. Bernhardt, D. J. Mcgillicuddy Jr., W. G. Zhang, P. D. Chappell

OES Faculty Publications

Continental shelves contribute a large fraction of the ocean's new nitrogen (N) via N2 fixation; yet, we know little about how physical processes at the ocean's margins shape diazotroph biogeography and activity. Here, we test the hypothesis that frontal mixing favors N2 fixation at the Mid-Atlantic Bight shelfbreak. Using the 15N2 bubble release method, we measured N2 fixation rates on repeat cross-frontal transects in July 2019. N2 fixation rates in shelf waters (median = 5.42 nmol N L−1 d−1) were higher than offshore (2.48 nmol N L−1 d−1) …

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Ice-Marginal Lava Delta In Iceland Found On A Nondescript Shallow Slope: An Unexpected Record Of Ice Thickness Late In Deglacian, Audrey Putnam, Kirsten Siebach, Candice Bedford, Sarah Simpson, Elizabeth Rampe, Joseph Tamborski, Michael Thorpe Jan 2024

Ice-Marginal Lava Delta In Iceland Found On A Nondescript Shallow Slope: An Unexpected Record Of Ice Thickness Late In Deglacian, Audrey Putnam, Kirsten Siebach, Candice Bedford, Sarah Simpson, Elizabeth Rampe, Joseph Tamborski, Michael Thorpe

OES Faculty Publications

Volcanism increases when glaciers melt because isostatic rebound during deglaciation decreases the pressure on the mantle, which enhances decompression melting. Anthropogenic climate change is now causing ice sheets and valley glaciers to melt around the world and this deglaciation could stimulate volcanic activity and associated hazards in Iceland, Antarctica, Alaska, and Patagonia. However, current model predictions for volcanic activity associated with anthropogenic deglaciation in Iceland are poorly constrained, in part due to uncertainties in past volcanic output over time compared to ice sheet arrangements. Further work specifically characterizing glaciovolcanic and ice-marginal volcanoes in Iceland is needed to reconstruct volcanic output …

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4500-Year Paleohurricane Record From The Western Gulf Of Mexico, Coastal Central Tx, Usa, Sarah B. Monica, Davin J. Wallace, Elizabeth J. Wallace, Xiaojing Du, Sylvia G. Dee, John B. Anderson Jan 2024

4500-Year Paleohurricane Record From The Western Gulf Of Mexico, Coastal Central Tx, Usa, Sarah B. Monica, Davin J. Wallace, Elizabeth J. Wallace, Xiaojing Du, Sylvia G. Dee, John B. Anderson

OES Faculty Publications

Texas receives the second-highest number of tropical cyclone (TC) landfalls per year in the United States. At present, long-term TC projections from climate models remain uncertain due to the short and biased nature of Atlantic TC observations. Sediment archives of past storms can help extend the observational record of TC strikes over the past few millennia. When a TC makes landfall along the central Texas coast, coastal downwelling channels and storm currents transport and deposit coarse sediment to a zone of rapid accumulation along the shelf, known as the Texas Mud Blanket (TMB). This “backwash” process results in expansive storm …

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Global Subterranean Estuaries Modify Groundwater Nutrient Loading To The Ocean, Stephanie J. Wilson, Amy Moody, Tristan Mckenzie, M. Bayani Cardenas, Elco Luijendijk, Audrey H. Sawyer, Alicia Wilson, Holly A. Michael, Bochao Xu, Karen L. Knee, Hyung-Mi Cho, Yishai Weinstein, Adina Paytan, Nils Moosdorf, Chen-Tung Aurthur Chen, Melanie Beck, Cody Lopez, Dorina Murgulet, Guebuem Kim, Mathew A. Charette, Hannelore Waska, J. Severino P. Ibánhez, Gwénaëlle Chaillou, Till Oehler, Shin-Ichi Onodera, Mitsuyo Saito, Valenti Rodellas, Natasha Dimova, Daniel Montiel, Henrietta Dulai, Christina Richardson, Jinzhou Du, Eric Petermann, Xiaogang Chen, Kay L. Davis, Sebastien Lamontagne, Ryo Sugimoto, Guizhi Wang, Hailong Li, Américo I. Torres, Cansu Demir, Emily Bristol, Craig T. Connolly, James W. Mcclelland, Brenno J. Silva, Douglas Tait, Bsk Kumar, R. Viswanadham, Vvss Sarma, Emmanoel Silva-Filho, Alan Shiller, Alanna Lecher, Joseph Tamborski, Henry Bokuniewicz, Carlos Rocha, Anja Reckhardt, Michael Ernst Böttcher, Shan Jiang, Laura M. Hernández-Terrones, Suresh Babu, Beata Szmczycha, Mahmood Sadat-Noori, Felipe Niencheski, Kimberly Null, Craig Tobias, Bongkeun Song, Iris C. Anderson, Isaac R. Santos Jan 2024

Global Subterranean Estuaries Modify Groundwater Nutrient Loading To The Ocean, Stephanie J. Wilson, Amy Moody, Tristan Mckenzie, M. Bayani Cardenas, Elco Luijendijk, Audrey H. Sawyer, Alicia Wilson, Holly A. Michael, Bochao Xu, Karen L. Knee, Hyung-Mi Cho, Yishai Weinstein, Adina Paytan, Nils Moosdorf, Chen-Tung Aurthur Chen, Melanie Beck, Cody Lopez, Dorina Murgulet, Guebuem Kim, Mathew A. Charette, Hannelore Waska, J. Severino P. Ibánhez, Gwénaëlle Chaillou, Till Oehler, Shin-Ichi Onodera, Mitsuyo Saito, Valenti Rodellas, Natasha Dimova, Daniel Montiel, Henrietta Dulai, Christina Richardson, Jinzhou Du, Eric Petermann, Xiaogang Chen, Kay L. Davis, Sebastien Lamontagne, Ryo Sugimoto, Guizhi Wang, Hailong Li, Américo I. Torres, Cansu Demir, Emily Bristol, Craig T. Connolly, James W. Mcclelland, Brenno J. Silva, Douglas Tait, Bsk Kumar, R. Viswanadham, Vvss Sarma, Emmanoel Silva-Filho, Alan Shiller, Alanna Lecher, Joseph Tamborski, Henry Bokuniewicz, Carlos Rocha, Anja Reckhardt, Michael Ernst Böttcher, Shan Jiang, Laura M. Hernández-Terrones, Suresh Babu, Beata Szmczycha, Mahmood Sadat-Noori, Felipe Niencheski, Kimberly Null, Craig Tobias, Bongkeun Song, Iris C. Anderson, Isaac R. Santos

OES Faculty Publications

Terrestrial groundwater travels through subterranean estuaries before reaching the sea. Groundwater-derived nutrients drive coastal water quality, primary production, and eutrophication. We determined how dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP), and dissolved organic nitrogen (DON) are transformed within subterranean estuaries and estimated submarine groundwater discharge (SGD) nutrient loads compiling > 10,000 groundwater samples from 216 sites worldwide. Nutrients exhibited complex, nonconservative behavior in subterranean estuaries. Fresh groundwater DIN and DIP are usually produced, and DON is consumed during transport. Median total SGD (saline and fresh) fluxes globally were 5.4, 2.6, and 0.18 Tmol yr−1 for DIN, DON, and DIP, …

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An Inconsistent Enso Response To Northern Hemisphere Stadials Over The Last Deglaciation, Ryan H. Glaubke, Matthew W. Schmidt, Jennifer E. Hertzberg, Lenzie G. Ward, Franco Marcantonio, Danielle Schimmenti, Kaustubh Thirumalai Jan 2024

An Inconsistent Enso Response To Northern Hemisphere Stadials Over The Last Deglaciation, Ryan H. Glaubke, Matthew W. Schmidt, Jennifer E. Hertzberg, Lenzie G. Ward, Franco Marcantonio, Danielle Schimmenti, Kaustubh Thirumalai

OES Faculty Publications

The dynamics shaping the El Niño-Southern Oscillation's (ENSO) response to present and future climate change remain unclear, partly due to limited paleo-ENSO records spanning past abrupt climate events. Here, we measure Mg/Ca ratios on individual foraminifera to reconstruct east Pacific subsurface temperature variability, a proxy for ENSO variability, across the last 25,000 years, including the millennial-scale events of the last deglaciation. Combining these data with proxy system model output reveals divergent ENSO responses to Northern Hemisphere stadials: enhanced variability during Heinrich Stadial 1 (H1) and reduced variability during the Younger Dryas (YD), relative to the Holocene. H1 ENSO likely intensified …

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Different Visions From Biosview: A Brief Report, Lucas N. Potter, Xavier-Lewis Palmer Jan 2024

Different Visions From Biosview: A Brief Report, Lucas N. Potter, Xavier-Lewis Palmer

Electrical & Computer Engineering Faculty Publications

In this collaborative research endeavor at the intersection of biological safety and cybersecurity for BiosView labs, the authors highlight their engagement with a diverse student cohort. The chapter delves into the motivation behind collaborations extending beyond traditional academic research environments, emphasizing inclusivity. The meticulous examination of student demographics, including gender, self-reported ethnicity, and national origin, is detailed in the methodology. A student-centric approach is central to the exploration, focusing on aligning teaching and management styles with unique student needs. The chapter elaborates on effective teaching methodologies and management practices tailored for BiosView labs. A dedicated section emphasizes the purpose of …

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Widespread Crab Burrows Enhance Greenhouse Gas Emissions From Coastal Blue Carbon Ecosystems, Kai Xiao, Yuchen Wu, Feng Pan, Yingrong Huang, Hebo Peng, Meiqing Lu, Yan Zhang, Hailong Li, Yan Zheng, Chunmiao Zheng, Yan Liu, Nengwan Chen, Leilei Xiao, Guangxuan Han, Yasong Li, Pei Xin, Ruili Li, Bochao Xu, Faming Wang, Joseph J. Tamborski, Alicia M. Wilson, Daniel M. Alongi, Isaac R. Santos Jan 2024

Widespread Crab Burrows Enhance Greenhouse Gas Emissions From Coastal Blue Carbon Ecosystems, Kai Xiao, Yuchen Wu, Feng Pan, Yingrong Huang, Hebo Peng, Meiqing Lu, Yan Zhang, Hailong Li, Yan Zheng, Chunmiao Zheng, Yan Liu, Nengwan Chen, Leilei Xiao, Guangxuan Han, Yasong Li, Pei Xin, Ruili Li, Bochao Xu, Faming Wang, Joseph J. Tamborski, Alicia M. Wilson, Daniel M. Alongi, Isaac R. Santos

OES Faculty Publications

Fiddler crabs, as coastal ecosystem engineers, play a crucial role in enhancing biodiversity and accelerating the flow of material and energy. Here we show how widespread crab burrows modify the carbon sequestration capacity of different habitats across a large climatic gradient. The process of crab burrowing results in the reallocation of sediment organic carbon and humus. Crab burrows can increase more greenhouse gases emissions compared to the sediment matrix (CO2: by 17-30%; CH4: by 49-141%). Straightforward calculations indicate that these increased emissions could offset 35-134% of sediment carbon burial in these two ecosystems. This research highlights the complex interactions between …

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Removing Development Incentives In Risky Areas Promotes Climate Adaptation, Hannah Druckenmiller, Yanjun (Penny) Liao, Sophie Pesek, Margaret Walls, Shan Zhang Jan 2024

Removing Development Incentives In Risky Areas Promotes Climate Adaptation, Hannah Druckenmiller, Yanjun (Penny) Liao, Sophie Pesek, Margaret Walls, Shan Zhang

Economics Faculty Publications

As natural disasters grow in frequency and intensity with climate change, limiting the populations and properties in harm’s way will be key to adaptation. This study evaluates one approach to discouraging development in risky areas—eliminating public incentives for development, such as infrastructure investments, disaster assistance and federal flood insurance. Using machine learning and matching techniques, we examine the Coastal Barrier Resources System (CBRS), a set of lands where these federal incentives have been removed. We find that the policy leads to lower development densities inside designated areas, increases development in neighbouring areas, reduces flood damages and alters local demographics. Our …

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Delayed Coastal Inundations Caused By Ocean Dynamics Post-Hurricane Matthew, Kyungmin Park, Emanuele Di Lorenzo, Yinglong J. Zhang, Tal Ezer, Fei Yi Jan 2024

Delayed Coastal Inundations Caused By Ocean Dynamics Post-Hurricane Matthew, Kyungmin Park, Emanuele Di Lorenzo, Yinglong J. Zhang, Tal Ezer, Fei Yi

CCPO Publications

Post Hurricane Abnormal Water Level (PHAWL) poses a persistent inundation threat to coastal communities, yet unresolved knowledge gaps exist regarding its spatiotemporal impacts and causal mechanisms. Using a high-resolution coastal model with a set of observations, we find that the PHAWLs are up to 50 cm higher than the normal water levels for several weeks and cause delayed inundations around residential areas of the U.S. Southeast Coast (USSC). Numerical experiments reveal that while atmospheric forcing modulates the coastal PHAWLs, ocean dynamics primarily driven by the Gulf Stream control the mean component and duration of the shelf-scale PHAWLs. Because of the …

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Western Boundary Current-Subtropical Continental Shelf Interactions, William B. Savidge, Dana K. Savidge, Frederico Brandini, Adam T. Greer, Eileen E. Hofmann, Moninya Roughan, Iison De Silvera, Iain M. Suthers Jan 2024

Western Boundary Current-Subtropical Continental Shelf Interactions, William B. Savidge, Dana K. Savidge, Frederico Brandini, Adam T. Greer, Eileen E. Hofmann, Moninya Roughan, Iison De Silvera, Iain M. Suthers

CCPO Publications

Western boundary currents (WBCs) adjacent to subtropical continental shelves (STCSs; between ~25° and 35° latitude; Figure 1) transport heat, nutrients, and biota poleward along the western margins of major ocean basins, interacting with the continental margins and influencing their physics and biology. Eddies and meanders along the shelf edge upwell deep, nutrient-laden water that can be advected onto the adjacent shelves with a corresponding export of particle-rich shelf water (e.g., Lee et al., 1991; Kimura et al., 1997; Campos et al., 2000; Roughan and Middleton, 2002, 2004; Lutjeharms, 2006; Savidge and Savidge, 2014). Despite their similarities, the various STCS regions …

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Advancing Household Robotics: Deep Interactive Reinforcement Learning For Efficient Training And Enhanced Performance, Arpita Soni, Sujatha Alla, Suresh Dodda, Hemanth Volikatla Jan 2024

Advancing Household Robotics: Deep Interactive Reinforcement Learning For Efficient Training And Enhanced Performance, Arpita Soni, Sujatha Alla, Suresh Dodda, Hemanth Volikatla

Engineering Management & Systems Engineering Faculty Publications

The market for domestic robots—made to perform household chore, is growing as these robots relieve people of everyday responsibilities. Domestic robots are generally welcomed for their role in easing human labour, in contrast to industrial robots, which are frequently criticised for displacing human workers. But before these robots can carry out domestic chores, they need to become proficient in a number of minor activities, such as recognizing their surroundings, making decisions, and picking up on human behaviours. Reinforcement learning, or RL, has emerged as a key robotics technology that enables robots to interact with their environment and learn how to …

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Utility In Time Description In Priority Best-Worst Discrete Choice Models: An Empirical Evaluation Using Flynn's Data, Sasanka Adikari, Norou Diawara Jan 2024

Utility In Time Description In Priority Best-Worst Discrete Choice Models: An Empirical Evaluation Using Flynn's Data, Sasanka Adikari, Norou Diawara

Mathematics & Statistics Faculty Publications

Discrete choice models (DCMs) are applied in many fields and in the statistical modelling of consumer behavior. This paper focuses on a form of choice experiment, best-worst scaling in discrete choice experiments (DCEs), and the transition probability of a choice of a consumer over time. The analysis was conducted by using simulated data (choice pairs) based on data from Flynn's (2007) 'Quality of Life Experiment'. Most of the traditional approaches assume the choice alternatives are mutually exclusive over time, which is a questionable assumption. We introduced a new copula-based model (CO-CUB) for the transition probability, which can handle the dependent …

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Sparse Representer Theorems For Learning In Reproducing Kernel Banach Spaces, Rui Wang, Yuesheng Xu, Mingsong Yan Jan 2024

Sparse Representer Theorems For Learning In Reproducing Kernel Banach Spaces, Rui Wang, Yuesheng Xu, Mingsong Yan

Mathematics & Statistics Faculty Publications

Sparsity of a learning solution is a desirable feature in machine learning. Certain reproducing kernel Banach spaces (RKBSs) are appropriate hypothesis spaces for sparse learning methods. The goal of this paper is to understand what kind of RKBSs can promote sparsity for learning solutions. We consider two typical learning models in an RKBS: the minimum norm interpolation (MNI) problem and the regularization problem. We first establish an explicit representer theorem for solutions of these problems, which represents the extreme points of the solution set by a linear combination of the extreme points of the subdifferential set, of the norm function, …

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Weak-Strong Beam-Beam Simulation With Crab Cavity Noises For The Hadron Storage Ring Of The Electron-Ion Collider, Y. Luo, B. Gamage, C. Montag, D. Marx, D. Xu, F. Willeke, H. Huang, H. Lovelace Iii, J. Berg, M. Blaskiewicz, S. Peggs, T. Satogata, V. Ptitsyn, V. Morozov, Y. Hao Jan 2024

Weak-Strong Beam-Beam Simulation With Crab Cavity Noises For The Hadron Storage Ring Of The Electron-Ion Collider, Y. Luo, B. Gamage, C. Montag, D. Marx, D. Xu, F. Willeke, H. Huang, H. Lovelace Iii, J. Berg, M. Blaskiewicz, S. Peggs, T. Satogata, V. Ptitsyn, V. Morozov, Y. Hao

Mathematics & Statistics Faculty Publications

The Electron Ion Collider (EIC), to be constructed at Brookhaven National Laboratory, will collide polarized high-energy electron beams with hadron beams, achieving luminosities of up to 1 X 1034cm−2s−1 in the center-mass energy range of 20-140 GeV. Crab cavities are employed to compensate for the geometric luminosity loss caused by a large crossing angle of 25 mrad in the interaction region. The phase noise in crab cavities will induce a significant emittance growth for the hadron beams in the Hadron Storage Ring (HSR). Various models have been utilized to study the effects of crab cavity …

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Testing Informativeness Of Covariate-Induced Group Sizes In Clustered Data, Hasika K. Wickrama Senevirathne, Sandipan Duttta Jan 2024

Testing Informativeness Of Covariate-Induced Group Sizes In Clustered Data, Hasika K. Wickrama Senevirathne, Sandipan Duttta

Mathematics & Statistics Faculty Publications

Clustered data are a special type of correlated data where units within a cluster are correlated while units between different clusters are independent. The number of units in a cluster can be associated with that cluster’s outcome. This is called the informative cluster size (ICS), which is known to impact clustered data inference. However, when comparing the outcomes from multiple groups of units in clustered data, investigating ICS may not be enough. This is because the number of units belonging to a particular group in a cluster can be associated with the outcome from that group in that cluster, leading …

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Uniform Convergence Of Deep Neural Networks With Lipschitz Continuous Activation Functions And Variable Widths, Yuesheng Xu, Haizhang Zhang Jan 2024

Uniform Convergence Of Deep Neural Networks With Lipschitz Continuous Activation Functions And Variable Widths, Yuesheng Xu, Haizhang Zhang

Mathematics & Statistics Faculty Publications

We consider deep neural networks (DNNs) with a Lipschitz continuous activation function and with weight matrices of variable widths. We establish a uniform convergence analysis framework in which sufficient conditions on weight matrices and bias vectors together with the Lipschitz constant are provided to ensure uniform convergence of DNNs to a meaningful function as the number of their layers tends to infinity. In the framework, special results on uniform convergence of DNNs with a fixed width, bounded widths and unbounded widths are presented. In particular, as convolutional neural networks are special DNNs with weight matrices of increasing widths, we put …

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Machine-Learning-Enabled Diagnostics With Improved Visualization Of Disease Lesions In Chest X-Ray Images, Md. Fashiar Rahman, Tzu-Liang (Bill) Tseng, Michael Pokojovy, Peter Mccaffrey, Eric Walser, Scott Moen, Alex Vo, Johnny C. Ho Jan 2024

Machine-Learning-Enabled Diagnostics With Improved Visualization Of Disease Lesions In Chest X-Ray Images, Md. Fashiar Rahman, Tzu-Liang (Bill) Tseng, Michael Pokojovy, Peter Mccaffrey, Eric Walser, Scott Moen, Alex Vo, Johnny C. Ho

Mathematics & Statistics Faculty Publications

The class activation map (CAM) represents the neural-network-derived region of interest, which can help clarify the mechanism of the convolutional neural network’s determination of any class of interest. In medical imaging, it can help medical practitioners diagnose diseases like COVID-19 or pneumonia by highlighting the suspicious regions in Computational Tomography (CT) or chest X-ray (CXR) film. Many contemporary deep learning techniques only focus on COVID-19 classification tasks using CXRs, while few attempt to make it explainable with a saliency map. To fill this research gap, we first propose a VGG-16-architecture-based deep learning approach in combination with image enhancement, segmentation-based region …

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Adversarial Training Based Domain Adaptation Of Skin Cancer Images, Syed Qasim Gilani, Muhammad Umair, Maryam Naqvi, Oge Marques, Hee-Cheol Kim Jan 2024

Adversarial Training Based Domain Adaptation Of Skin Cancer Images, Syed Qasim Gilani, Muhammad Umair, Maryam Naqvi, Oge Marques, Hee-Cheol Kim

Electrical & Computer Engineering Faculty Publications

Skin lesion datasets used in the research are highly imbalanced; Generative Adversarial Networks can generate synthetic skin lesion images to solve the class imbalance problem, but it can result in bias and domain shift. Domain shifts in skin lesion datasets can also occur if different instruments or imaging resolutions are used to capture skin lesion images. The deep learning models may not perform well in the presence of bias and domain shift in skin lesion datasets. This work presents a domain adaptation algorithm-based methodology for mitigating the effects of domain shift and bias in skin lesion datasets. Six experiments were …

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Toward Inclusivity: Rethinking Islamophobic Content Classification In The Digital Age, Esraa Aldreabi, Mukul Dev Chhangani, Khawlah M. Harahsheh, Justin M. Lee, Chung-Hao Chen Jan 2024

Toward Inclusivity: Rethinking Islamophobic Content Classification In The Digital Age, Esraa Aldreabi, Mukul Dev Chhangani, Khawlah M. Harahsheh, Justin M. Lee, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

In this paper, we implement a comprehensive three-class system to categorize social media discussions about Islam and Muslims, enhancing the typical binary approach. These classes are: I) General Discourse About Islam and Muslims, II) Criticism of Islamic Teachings and Figures, and III) Comments Against Muslims. These categories are designed to balance the nuances of free speech while protecting diverse groups like Muslims, ex-Muslims, LGBTQ+ communities, and atheists. By utilizing machine learning and employing transformer-based models, we analyze the distribution and characteristics of these classes in social media content. Our findings reveal distinct patterns of user engagement with topics related to …

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Deapsecure Computational Training For Cybersecurity: Progress Toward Widespread Community Adoption, Wirawan Purwanto, Bahador Dodge, Karina Arcaute, Masha Sosonkina, Hongyi Wu Jan 2024

Deapsecure Computational Training For Cybersecurity: Progress Toward Widespread Community Adoption, Wirawan Purwanto, Bahador Dodge, Karina Arcaute, Masha Sosonkina, Hongyi Wu

Electrical & Computer Engineering Faculty Publications

The Data-Enabled Advanced Computational Training Program for Cybersecurity Research and Education (DeapSECURE) is a non-degree training consisting of six modules covering a broad range of cyberinfrastructure techniques, including high performance computing, big data, machine learning and advanced cryptography, aimed at reducing the gap between current cybersecurity curricula and requirements needed for advanced research and industrial projects. Since 2020, these lesson modules have been updated and retooled to suit fully-online delivery. Hands-on activities were reformatted to accommodate self-paced learning. In this paper, we summarize the four years of the project comparing in-person and on-line only instruction methods as well as outlining …

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Domain Adaptive Federated Learning For Multi-Institution Molecular Mutation Prediction And Bias Identification, W. Farzana, M. A. Witherow, I. Longoria, M. S. Sadique, A. Temtam, K. M. Iftekharuddin Jan 2024

Domain Adaptive Federated Learning For Multi-Institution Molecular Mutation Prediction And Bias Identification, W. Farzana, M. A. Witherow, I. Longoria, M. S. Sadique, A. Temtam, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Deep learning models have shown potential in medical image analysis tasks. However, training a generalized deep learning model requires huge amounts of patient data that is usually gathered from multiple institutions which may raise privacy concerns. Federated learning (FL) provides an alternative to sharing data across institutions. Nonetheless, FL is susceptible to a few challenges including inversion attacks on model weights, heterogenous data distributions, and bias. This study addresses heterogeneity and bias issues for multi-institution patient data by proposing domain adaptive FL modeling using several radiomics (volume, fractal, texture) features for O6-methylguanine-DNA methyltransferase (MGMT) classification across multiple institutions. The proposed …

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Using Feature Selection Enhancement To Evaluate Attack Detection In The Internet Of Things Environment, Khawlah Harahsheh, Rami Al-Naimat, Chung-Hao Chen Jan 2024

Using Feature Selection Enhancement To Evaluate Attack Detection In The Internet Of Things Environment, Khawlah Harahsheh, Rami Al-Naimat, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The rapid evolution of technology has given rise to a connected world where billions of devices interact seamlessly, forming what is known as the Internet of Things (IoT). While the IoT offers incredible convenience and efficiency, it presents a significant challenge to cybersecurity and is characterized by various power, capacity, and computational process limitations. Machine learning techniques, particularly those encompassing supervised classification techniques, offer a systematic approach to training models using labeled datasets. These techniques enable intrusion detection systems (IDSs) to discern patterns indicative of potential attacks amidst the vast amounts of IoT data. Our investigation delves into various aspects …

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Quest For An Optimal Spin-Polarized Electron Source For The Electron-Ion Collider, J. Biswas, E. Wang, O. Rahman, J. Sharitka, K. Kisslinger, Adam Masters, S. Marsillac, T. Lee Jan 2024

Quest For An Optimal Spin-Polarized Electron Source For The Electron-Ion Collider, J. Biswas, E. Wang, O. Rahman, J. Sharitka, K. Kisslinger, Adam Masters, S. Marsillac, T. Lee

Electrical & Computer Engineering Faculty Publications

Superlattice GaAs photocathodes play a crucial role as the primary source of polarized electrons in various accelerator facilities, including the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson National Laboratory and the Electron-Ion Collider (EIC) at Brookhaven National Laboratory. To increase the quantum efficiency (QE) of GaAs/GaAsP superlattice photocathodes, a Distributed Bragg Reflector (DBR) is grown underneath using metal-organic chemical vapor deposition (MOCVD). There are several challenges associated with DBR photocathodes: the resonance peak may not align with the emission threshold of around 780 nm, non-uniform doping density in the top 5 nm may significantly impact QE and spin polarization, …

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Beyond Binary: Revealing Variations In Islamophobic Content With Hierarchical Multi-Class Classification, Esraa Aldreabi, Khawlah M. Harahsheh, Mukul Dev Chhangani, Chung-Hao Chen, Jeremy Blackburn Jan 2024

Beyond Binary: Revealing Variations In Islamophobic Content With Hierarchical Multi-Class Classification, Esraa Aldreabi, Khawlah M. Harahsheh, Mukul Dev Chhangani, Chung-Hao Chen, Jeremy Blackburn

Electrical & Computer Engineering Faculty Publications

In the digital age, the rise of Islamophobia-marked by an irrational fear or discrimination against Islam and Muslims-has emerged as a pressing issue, especially on social media platforms. In this paper we employs a multi-class classification system, moving beyond traditional binary models. We categorize Islamophobic content into three main classes and various subclasses, covering a range from subtle biases to explicit incitement. Comparative analysis of data from Reddit and Twitter illuminates the distinct prevalence and types of Islamophobic content specific to each platform. This paper deepens our understanding of digital Islamophobia and provides insights for crafting targeted online counter strategies. …

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Runtime Performance Of Gamess Quantum Chemistry Application Offloaded To Gpus, Masha Sosonkina, Gabriel Mateescu, Peng Xu, Tosaporn Sattasathuchana, Buu Pham, Mark S. Gordon, Sarom S. Leang Jan 2024

Runtime Performance Of Gamess Quantum Chemistry Application Offloaded To Gpus, Masha Sosonkina, Gabriel Mateescu, Peng Xu, Tosaporn Sattasathuchana, Buu Pham, Mark S. Gordon, Sarom S. Leang

Electrical & Computer Engineering Faculty Publications

Computational chemistry is at the forefront of solving urgent societal problems, such as polymer upcycling and carbon capture. The complexity of modeling these processes at appropriate length and time scales is mainly manifested in the number and types of chemical species involved in the reactions and may require models of several thousand atoms and large basis sets to accurately capture the chemical complexity and heterogeneity in the physical and chemical processes. The quantum chemistry package General Atomic and Molecular Electronic Structure System (GAMESS) has a wide array of methods that can efficiently and accurately treat complex chemical systems. In this …

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Ensemble Learning With Sleep Mode Management To Enhance Anomaly Detection In Iot Environment, Khawlah Harahsheh, Rami Al-Naimat, Malek Alzaqebah, Salam Shreem, Esraa Aldreabi, Chung-Hao Chen Jan 2024

Ensemble Learning With Sleep Mode Management To Enhance Anomaly Detection In Iot Environment, Khawlah Harahsheh, Rami Al-Naimat, Malek Alzaqebah, Salam Shreem, Esraa Aldreabi, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The rapid proliferation of Internet of Things (IoT) devices has underscored the critical need for energy-efficient cybersecurity measures. This presents the dual challenge of maintaining robust security while minimizing power consumption. Thus, this paper proposes enhancing the machine learning performance through Ensemble Techniques with Sleep Mode Management (ELSM) approach for IoT Intrusion Detection Systems (IDS). The main challenge lies in the high-power consumption attributed to continuous monitoring in traditional IDS setups. ELSM addresses this challenge by introducing a sophisticated sleep-awake mechanism, activating the IDS system only during anomaly detection events, effectively minimizing energy expenditure during periods of normal network operation. …

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Ground Tire Rubber As A Sustainable Additive: Transforming Desert Sand Behavior, Nabil Ismael, Dalya Ismael, Asmaa Al-Ahmad Jan 2024

Ground Tire Rubber As A Sustainable Additive: Transforming Desert Sand Behavior, Nabil Ismael, Dalya Ismael, Asmaa Al-Ahmad

Engineering Technology Faculty Publications

Managing waste tires presents a significant challenge globally, particularly in regions experiencing high temperatures and shortage of landfill sites. This issue is affecting countries like Kuwait, where the abundance of waste tires is a major source of environmental and safety risks, particularly during the intensely hot summer months. This extreme heat has sparked numerous fires, leading to substantial air pollution due to thick black smoke. Given the limited disposal options, recycling waste tires and finding practical applications for ground tire rubber (GTR) is essential. To address the challenge, a comprehensive laboratory testing program was conducted, using locally produced rubber aggregates …

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Types Of Teacher-Ai Collaboration In K-12 Classroom Instruction: Chinese Teachers' Perspective, Jinhee Kim Jan 2024

Types Of Teacher-Ai Collaboration In K-12 Classroom Instruction: Chinese Teachers' Perspective, Jinhee Kim

STEMPS Faculty Publications

The advancing power and capabilities of artificial intelligence (AI) have expanded the roles of AI in education and have created the possibility for teachers to collaborate with AI in classroom instruction. However, the potential types of teacher-AI collaboration (TAC) in classroom instruction and the benefits and challenges of implementing TAC are still elusive. This study, therefore, aimed to explore different types of TAC and the potential benefits and obstacles of TAC through Focus Group Interviews with 30 Chinese teachers. The study found that teachers anticipated six types of TAC, which are thematized as One Teach, One Observe; One Teach, One …

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The Role Of Shopping Orientations And Intrinsic Experiential Value In Consumer's Willingness To Follow Embodied-Ai's Advice In Fashion Shoe Stores, Christina Soyoung Song, Ji Young Lee, Dooyoung Choi Jan 2024

The Role Of Shopping Orientations And Intrinsic Experiential Value In Consumer's Willingness To Follow Embodied-Ai's Advice In Fashion Shoe Stores, Christina Soyoung Song, Ji Young Lee, Dooyoung Choi

STEMPS Faculty Publications

This study employs a synthesis of Intrinsic Motivation Theory with three shopping orientations, namely “adventure,” “idea,” and “personalized” shopping, in order to examine their potential influence on individuals' motivation towards shopping. We proposed that consumers’ experiential value of intrinsic enjoyment is an indispensable mediator that affects their willingness to follow EAI’s advice. The study offers novel insights into the way that consumers’ characteristics of influencing others’ clothing consumption affect their shopping motivations to find adventure and stimulation, keep up with new fashion trends and products information, and their preference to patronize stores and interact with store staff on a personal …

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Ai-Designed Clothing And Perceived Values: What Can Move Consumers' Minds With The Ai-Designed Clothing?, Choi Dooyoung, Ha Kyung Lee Jan 2024

Ai-Designed Clothing And Perceived Values: What Can Move Consumers' Minds With The Ai-Designed Clothing?, Choi Dooyoung, Ha Kyung Lee

STEMPS Faculty Publications

This study investigates the perceived values of AI-designed clothing (quality, emotion, ease) and their impact on willingness to pay (WTP) and word-of-mouth (WOM), with the moderating effect of gender differences. A total of 314 respondents completed the survey via MTurk. Participants watched a video clip demonstrating how an AI system creates various clothing designs by altering garment elements (e.g., style, size). After watching the video clip, they were asked to answer a series of questions about the AI-designed clothing and themselves. The collected data were analyzed using AMOS 26.0. Results showed that, for male and female consumers, the quality value …

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