Physical Sciences and Mathematics Commons^™

Scene Understanding And Spatial Analysis Using Scene Graph Enhanced By Hall's Proxemics Zones In Smart Homes, Debaleen Das Spandan

MSU Graduate Theses

Voice-controlled smart assistants have received widespread popularity. It plays a pivotal role in smart homes by providing a natural and convenient interface for interacting with smart devices. However, these assistants are unable to serve persons with physical disabilities and speech impairments. Therefore, non-verbal communication methods, such as eye tracking, gesture recognition, and context awareness can complement and overcome some of these limitations to enhance user experience in smart homes. To address this issue, I am investigating non-verbal communication methods to make smart home technology more accessible and intuitive. In this research, I focus on proxemics, i.e., the study of distance …

Disaggregating Longer-Term Trends From Seasonal Variations In Measured Pv System Performance, Chibuisi Chinasaokwu Okorieimoh, Brian Norton, Michael Conlon

Articles

Photovoltaic (PV) systems are widely adopted for renewable energy generation, but their performance is influenced by complex interactions between longer-term trends and seasonal variations. This study aims to remove these factors and provide valuable insights for optimising PV system operation. We employ comprehensive datasets of measured PV system performance over five years, focusing on identifying the distinct contributions of longer-term trends and seasonal effects. To achieve this, we develop a novel analytical framework that combines time series and statistical analytical techniques. By applying this framework to the extensive performance data, we successfully break down the overall PV system output into …

Integrating Remote Sensing With Ground-Based Observations To Quantify The Effects Of An Extreme Freeze Event On Black Mangroves (Avicennia Germinans) At The Landscape Scale, Melinda Martinez, Michael J. Osland, James B. Grace, Nicholas M. Enwright, Camille L. Stagg, Camille L. Stagg, Simen Kaalstad, Gordon H. Anderson, Elena A. Flores, Alejandro Fierro-Cabo

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

Climate change is altering the frequency and intensity of extreme weather events. Quantifying ecosystem responses to extreme events at the landscape scale is critical for understanding and responding to climate-driven change but is constrained by limited data availability. Here, we integrated remote sensing with ground-based observations to quantify landscape-scale vegetation damage from an extreme climatic event. We used ground- and satellite-based black mangrove (Avicennia germinans) leaf damage data from the northern Gulf of Mexico (USA and Mexico) to examine the effects of an extreme freeze in a region where black mangroves are expanding their range. The February 2021 …

Managing Inter-Organizational Trust And Risk Perceptions In Transboundary Fisheries Governance Networks, Evelyn Roozee, Dongkyu Kim, Antonia Sohns, Jasper R. De Vries, Owen Temby, Gordon M. Hickey

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

Transboundary fishery management represents a significant governance challenge that requires ongoing inter-organizational communication, collaboration, and collective action to ensure sustainability. Previous research suggests that different dimensions of perceived risk, trust, and control interact in complex ways to affect inter-organizational collaborative performance, providing an administrative ‘architecture’ that enables partners to share resources, engage in teamwork, resolve conflict, and coordinate tasks and responsibilities while also allaying their concerns about the alliance. However, the extent to which different control mechanisms influence trust and mitigate the perceived risks of collaboration between the diverse organizations involved in transboundary fisheries management remains unclear. This paper presents …

Influence Of Attack Performance On The Ovc Volleyball Regular Seasons 2022 & 2023, Ignacio Valdemoros

Masters Theses

Understanding the outcome of volleyball games is necessary for coaches before, after, and during a season. There are several ways to gain this understanding, but statistical analysis is fundamental to see the minimum patterns of behavior that influence wins and losses in Volleyball. Furthermore, this analysis helps identify the optimal approach to achieving a goal and determining the most effective alternative to success. Scoring points in Volleyball involves three key skills: serving, blocking, and attacking. Among these skills, attacking plays the most relevant role in determining the outcome of a match. The position on the court (e.g. Outside Hitter, Middle …

Ultradeep Atca Imaging Of 47 Tucanae Reveals A Central Compact Radio Source, Alessandro Paduano, Arash Bahramian, James C. A. Miller-Jones, Adela Kawka, Tim J. Galvin, Liliana E. Rivera Sandoval, Sebastian Kamann, Jay Strader, Laura Chomiuk, Craig O. Heinke

Physics and Astronomy Faculty Publications and Presentations

We present the results of an ultradeep radio continuum survey, containing ∼480 hr of observations, of the Galactic globular cluster 47 Tucanae with the Australia Telescope Compact Array. This comprehensive coverage of the cluster allows us to reach rms noise levels of 1.19 μJy beam−1 at 5.5 GHz, 940 nJy beam−1 at 9 GHz, and 790 nJy beam−1 in a stacked 7.25 GHz image. This is the deepest radio image of a globular cluster and the deepest image ever made with the Australia Telescope Compact Array. We identify ATCA J002405.702-720452.361, a faint (6.3 ± 1.2 μJy at 5.5 …

Glitch Veto Based On Unphysical Gravitational Wave Binary Inspiral Templates, Raghav Girgaonkar, Soumya D. Mohanty

Physics and Astronomy Faculty Publications and Presentations

Transient signals arising from instrumental or environmental factors, commonly referred to as glitches, constitute the predominant background of false alarms in gravitational wave searches with ground-based detectors. Therefore, effective data analysis methods for vetoing glitch-induced false alarms are crucial to enhancing the sensitivity of a search. We present a veto method for glitches that impact matched filtering-based searches for binary inspiral signals. The veto uses unphysical sectors in the space of chirp time parameters as well as an unphysical extension including negative chirp times to efficiently segregate glitches from gravitational wave signals in data from a single detector. Inhabited predominantly …

Towards Explainable Neural Network Fairness, Mengdi Zhang

Dissertations and Theses Collection (Open Access)

Neural networks are widely applied in solving many real-world problems. At the same time, they are shown to be vulnerable to attacks, difficult to debug, non-transparent and subject to fairness issues. Discrimination has been observed in various machine learning models, including Large Language Models (LLMs), which calls for systematic fairness evaluation (i.e., testing, verification or even certification) before their deployment in ethic-relevant domains. If a model is found to be discriminating, we must apply systematic measure to improve its fairness. In the literature, multiple categories of fairness improving methods have been discussed, including pre-processing, in-processing and post-processing.
In this dissertation, …

Examining Stigma In Rural Mental Health Care Settings: A Mixed Methods Approach, Lainie Krumenacker

Murray State Theses and Dissertations

More than half of Americans will be diagnosed with a mental illness in their lifetime (CDC, 2021), yet stigma towards mental health affects both patients and providers. Although programs exist to address stigma, improve cultural competency among providers, and educate families on the importance of support, facilities are often limited on programs they provide due to allocation of resources and funds. Without a shift in treatment and programing, stigma will continue to impact patient care and outcome.

This study explored stigma among mental health providers in rural communities, while exploring potential differences in treatment among patients due to race. Mental …

Source Anisotropies And Pulsar Timing Arrays, Bruce Allen, Deepali Agarwal, Joseph D. Romano, Serena Valtolina

Physics and Astronomy Faculty Publications and Presentations

Pulsar timing arrays (PTA) hunt for gravitational waves (GW) by searching for the correlations that GWs induce in the time-of-arrival residuals from different pulsars. If the GW sources are of astrophysical origin, then they are located at discrete points on the sky. However, PTA data are often modeled, and subsequently analyzed, via a "standard Gaussian ensemble". That ensemble is obtained in the limit of an infinite density of vanishingly weak, Poisson-distributed sources. In this paper, we move away from that ensemble, to study the effects of two types of "source anisotropy". The first (a), which is often called "shot noise", …

Gnss Software Defined Radio: History, Current Developments, And Standardization Efforts, Thomas Pany, Dennis Akos, Javier Arribas, M. Zahidul H. Bhuiyan, Pau Closas, Fabio Dovis, Ignacio Fernandez-Hernandez, Carles Fernandez-Prades, Sanjeev Gunawardena, Todd Humphreys, Zaher M. Kassas, Jose A. Lopez Salcedo, Mario Nicola, Mario L. Psiaki, Alexander Rugamer, Yong-Jin Song, Jong-Hoon Won

Faculty Publications

Taking the work conducted by the global navigation satellite system (GNSS) software-defined radio (SDR) working group during the last decade as a seed, this contribution summarizes, for the first time, the history of GNSS SDR development. This report highlights selected SDR implementations and achievements that are available to the public or that influenced the general development of SDR. Aspects related to the standardization process of intermediate-frequency sample data and metadata are discussed, and an update of the Institute of Navigation SDR Standard is proposed. This work focuses on GNSS SDR implementations in general-purpose processors and leaves aside developments conducted on …

An Analysis Of Precision: Occlusion And Perspective Geometry’S Role In 6d Pose Estimation, Jeffrey Choate, Derek Worth, Scott Nykl, Clark N. Taylor, Brett J. Borghetti, Christine M. Schubert Kabban

Faculty Publications

Achieving precise 6 degrees of freedom (6D) pose estimation of rigid objects from color images is a critical challenge with wide-ranging applications in robotics and close-contact aircraft operations. This study investigates key techniques in the application of YOLOv5 object detection convolutional neural network (CNN) for 6D pose localization of aircraft using only color imagery. Traditional object detection labeling methods suffer from inaccuracies due to perspective geometry and being limited to visible key points. This research demonstrates that with precise labeling, a CNN can predict object features with near-pixel accuracy, effectively learning the distinct appearance of the object due to perspective …

In Pursuit Of Consumption-Based Forecasting, Charles Chase, Kenneth B. Kahn

Marketing Faculty Publications

[Introduction] Today's most mature, most sophisticated, best-in-class forecasting is what we call consumption-based forecasting (CBF). In contrast, the least sophisticated companies typically do not forecast at all, but rather set financial targets based on management expectations. Companies beginning to use statistical forecasting techniques usually take a supply-centric orientation, relying on time series techniques applied to shipment and/or order history. The next stage of progression is to incorporate promotions data, economic data, and market data alongside supply-centric data so that regression and other advanced analytics can be used. Companies pursing CBF utilize even more advanced capabilities to capture, examine, and understand …

Constrained Multiview Representation For Self-Supervised Contrastive Learning, Siyuan Dai, Kai Ye, Kun Zhao, Ge Cui, Haoteng Tang, Liang Zhan

Computer Science Faculty Publications and Presentations

Representation learning constitutes a pivotal cornerstone in contemporary deep learning paradigms, offering a conduit to elucidate distinctive features within the latent space and interpret the deep models. Nevertheless, the inherent complexity of anatomical patterns and the random nature of lesion distribution in medical image segmentation pose significant challenges to the disentanglement of representations and the understanding of salient features. Methods guided by the maximization of mutual information, particularly within the framework of contrastive learning, have demonstrated remarkable success and superiority in decoupling densely intertwined representations. However, the effectiveness of contrastive learning highly depends on the quality of the positive and …

A Simple Proof That Ricochet Robots Is Pspace-Complete, Jose Balanza-Martinez, Angel A. Cantu, Robert Schweller, Tim Wylie

Computer Science Faculty Publications and Presentations

In this paper, we seek to provide a simpler proof that the relocation problem in Ricochet Robots (Lunar Lockout with fixed geometry) is PSPACE-complete via a reduction from Finite Function Generation (FFG). Although this result was originally proven in 2003, we give a simpler reduction by utilizing the FFG problem, and put the result in context with recent publications showing that relocation is also PSPACE-complete in related models.

Efficient High-Resolution Time Series Classification Via Attention Kronecker Decomposition, Aosong Feng, Jialin Chen, Juan Garza, Brooklyn Berry, Francisco Salazar, Yifeng Gao, Rex Ying, Leandros Tassiulas

Computer Science Faculty Publications and Presentations

The high-resolution time series classification problem is essential due to the increasing availability of detailed temporal data in various domains. To tackle this challenge effectively, it is imperative that the state-of-theart attention model is scalable to accommodate the growing sequence lengths typically encountered in highresolution time series data, while also demonstrating robustness in handling the inherent noise prevalent in such datasets. To address this, we propose to hierarchically encode the long time series into multiple levels based on the interaction ranges. By capturing relationships at different levels, we can build more robust, expressive, and efficient models that are capable of …

Growing Up Sustainable? Politics Of Race And Youth In Urbanplan, Copenhagen, Max Ritts, Rebecca Rutt

Geography

This paper considers how racialized youth in Denmark negotiate sustainability amid contexts marked by intersecting forms of economic restructuring, progressive neoliberalism, white ethno-nationalism, and green urban planning. Urbanplan is a low-income, notoriously “troubled” Copenhagen neighborhood where we conducted fieldwork for 7 months (2019-2020) with fifteen male youth, aged 17-21. Using ethnography, policy reviews, and interviews with city social workers, we explore how intimate experiences of nature, group-identity, and place attachment here relate to and depart from the structural forces actively reshaping the neighborhood. Our analysis combines Cindi Katz's intersectional political economy approach with recent work on green gentrification, Critical Utopian …

Reducing Short-Chain Pfas Levels In California Water Supplies, Manu Prabandham

Pomona Senior Theses

This thesis proposes twelve specific policies based on precedents set by prior regulation of persistent organic pollutants (such as PCBs), the costs and benefits of short-chain PFAS, technologies available to remove and destroy short-chain PFAS, and the roles and limitations of California’s regulatory institutions. The twelve policies are chosen to be politically and financially feasible, effective at removing short-chain PFAS from water supplies, and equitable towards lower-income and minority Californians, who suffer the most from the consequences of PFAS and other environmental pollutants. The new definitions, education campaigns, studies, taxes, bans, standards, testing, and filtration systems proposed are intended to …

Mechanisms Of Dendritic Shorting In Lithium Metal Batteries With Li7la3zr2o12 Solid Electrolytes, Lukas Karapin-Springorum

Pomona Senior Theses

Energy storage will play a crucial role in efforts to mitigate the effects of climate change caused by greenhouse gas emissions from human activity. Lithium metal batteries using solid electrolytes like Li₇La₃Zr₂O₁₂ have higher energy density than lithium-ion batteries, which may enable a more rapid and complete electrification of transportation. However, lithium metal batteries suffer from undesirable short-circuiting when metallic lithium deposits connect the two electrodes. It has been debated whether these lithium dendrites generally grow directionally from the anode or are generated by the reduction of lithium ions inside the solid electrolyte, …

Advancements In Glitch Subtraction Systems For Enhancing Gravitational Wave Data Analysis: A Brief Review, Mohammad Abu Thaher Chowdhury

Physics and Astronomy Faculty Publications and Presentations

Glitches are transitory noise artifacts that degrade the detection sensitivity and accuracy of interferometric observatories such as LIGO and Virgo in gravitational wave astronomy. Reliable glitch subtraction techniques are essential for separating genuine gravitational wave signals from background noise and improving the accuracy of astrophysical investigations. This review study summarizes the main glitch subtraction methods used in the industry. We talk about the efficacy of classic time-domain techniques in real-time applications, like matched filtering and regression methods. The robustness of frequency-domain approaches, such as wavelet transformations and spectral analysis, in detecting and mitigating non-stationary glitches is assessed. We also investigate …

Self Pre-Training With Topology- And Spatiality-Aware Masked Autoencoders For 3d Medical Image Segmentation, Pengfei Gu, Yejia Zhang, Huimin Li, Chaoli Wang, Danny Z. Chen

Computer Science Faculty Publications and Presentations

Masked Autoencoders (MAEs) have been shown to be effective in pre-training Vision Transformers (ViTs) for natural and medical image analysis problems. By reconstructing missing pixel/voxel information in visible patches, a ViT encoder can aggregate contextual information for downstream tasks. But, existing MAE pre-training methods, which were specifically developed with the ViT architecture, lack the ability to capture geometric shape and spatial information, which is critical for medical image segmentation tasks. In this paper, we propose a novel extension of known MAEs for self pre-training (i.e., models pre-trained on the same target dataset) for 3D medical image segmentation. (1) We propose …

Complete Solution Of The Lady In The Lake Scenario, Alexander Von Moll, Meir Pachter

Faculty Publications

In the Lady in the Lake scenario, a mobile agent, L, is pitted against an agent, M, who is constrained to move along the perimeter of a circle. L is assumed to begin inside the circle and wishes to escape to the perimeter with some finite angular separation from M at the perimeter. This scenario has, in the past, been formulated as a zero-sum differential game wherein L seeks to maximize terminal separation and M seeks to minimize it. Its solution is well-known. However, there is a large portion of the state space for which the canonical solution does not …

Garbage In ≠ Garbage Out: Exploring Gan Resilience To Image Training Set Degradations, Nicholas Crino, Bruce A. Cox, Nathan B. Gaw

Faculty Publications

Generative Adversarial Networks (GANs) have received immense attention in recent years due to their ability to capture complex, high-dimensional data distributions without the need for extensive labeling. Since their conception in 2014, a wide array of GAN variants have been proposed featuring alternative architectures, optimizers, and loss functions with the goal of improving performance and training stability. This manuscript focuses on quantifying the resilience of a GAN architecture to specific modes of image degradation. We conduct systematic experimentation to empirically determine the effects of 10 fundamental image degradation modes, applied to the training image dataset, on the Fréchet inception distance …

Enhancing Decision-Making In Higher Education: Exploring The Integration Of Chatgpt And Data Visualization Tools In Data Analysis, Tristan Jiang, Elina Liu, Tasawar Baig, Qingrong Li

University Administration Publications

This chapter explores the potential of integrating conversational AI tools such as ChatGPT with data visualization (DV) tools such as Power BI in higher education settings. A brief history of chatbots is summarized and challenges and opportunities in higher education are outlined. The highlights include AI's prospects for enhancing data-informed decision-making while needing safeguards to mitigate risks. Through a pioneering exercise, we integrated ChatGPT's conversational capabilities with Power BI's interface via API and tested functionality. Suggestions for good practice and implications for higher education are discussed.

Using Phenology To Unravel Differential Soil Water Use And Productivity In A Semiarid Savanna, Blake Steiner, Russell L. Scott, Jia Hu, Natasha Mcbean, Andrew Richardson, David J. P. Moore

University Administration Publications

Savannas are water-limited ecosystems characterized by two dominant plant types: trees and an understory primarily made up grass. Different phenology and root structures of these plant types complicate how savanna primary productivity responds to changes in water availability. We tested the hypothesis that productivity in savannas is controlled by the temporal and vertical distribution of soil water content (SWC) and differences in growing season length of understory and tree plant functional types. To quantify the relationship between tree, understory, and savanna-wide phenology and productivity, we used PhenoCam and satellite observations surrounding an eddy covariance tower at a semiarid savanna site …

Synthesis And Characterization Of Quantum Materials, Yunsheng Qiu

Doctoral Dissertations

"In this study, attempts were made to grow quantum materials that have recently undergone a profound change of perspective. These materials are involved in intricate macroscopic properties rooted in the subtle nature of quantum physics. To explore our understanding of quantum materials, this study includes three projects: Magnetic Topological Insulators, Topological Superconductors, and high-temperature superconductors.

A Cr-doped Sb₂Te₃ is added to the category for the magnetic topological insulators project. Their transport properties are studied, and the origin of ferromagnetism is studied. Anomalous Hall effect is observed in the Hall measurements, and serval factors (cooling rate, dopant deficiency) …

Catalytic Control Of The Nanomorphology And Mechanical Properties Of Aliphatic Shape-Memory Aerogels, A B M Shaheen Ud Doulah

Doctoral Dissertations

"Shape-memory poly(isocyanurate-urethane) (PIR-PUR) aerogels are nanoporous solids that can return to their original shape after being compressed, through a heating actuation step. This thesis compares the effectiveness of various metal ions as catalysts in the formation of PIR-PUR aerogels, and explores the correlation between catalytic activity, nanomorphology, and mechanical properties of the resulting aerogels. The gelation rate was found to increase from Fe to Cu and then decline from Cu to Ga in the periodic table. CuCl₂ was found to be the fastest catalyst, and FeCl₃ the slowest. The morphology of the aerogels changed from bicontinuous to spheroidal …

Critical Behavior And Dynamics Of The Superfluid-Mott Glass Transition, Jack Russell Crewse

Doctoral Dissertations

This work studies the effects of disorder on the thermodynamic critical behavior and dynamical properties of the superfluid-Mott glass quantum phase transition. After a brief introduction covering relevant fundamentals, we present the dissertation in the form of four separate but related publications. In the first two publications, we calculate the thermodynamic critical exponents of the superfluid-Mott glass quantum phase transition in both two and three spatial dimensions. The undiluted transition exhibits critical exponents that violate the Harris criterion, and thus the critical behavior is expected to change upon introducing disorder. We confirm this behavior via Monte Carlo simulation of a …

High-Resolution Spectroscopy Of Interstellar Lines And Comets, Chemeda Tadese Ejeta

Doctoral Dissertations

"The study of interstellar molecules such as CO is crucial because interstellar ices in the core of a pre-solar molecular cloud provide the starting point for volatile evolution in the protoplanetary disk. A record of the initial volatile composition of the protoplanetary disk can be obtained from the study of the chemical composition of cometary nuclei. Because of their long residence in the Oort cloud and infrequent passage through the inner solar system, long-period comets are one of the most primitive bodies in our solar system that can tell us about the composition of the early solar system. High-resolution infrared …

The Impact Of Environmental Policy On Renewable Energy Innovation: A Systematic Literature Review And Research Directions, Hiva Rastegar, Gabriel Eweje, Aymen Sajjad

Research outputs 2022 to 2026

Renewable energy innovations are imperative to tackle the climate change crisis. However, there is a gap in the literature regarding the effectiveness of environmental policies in promoting renewable energy innovations. To bridge this gap, we have adopted a systematic literature review process covering the period from 2005 to 2023. We identified and analysed 29 articles in our final sample. Further, we employ two levels of analysis (individual-policy and policy-mix levels) for analysing the extant research. Our findings show that fiscal incentives and emissions trading policies such as the European Union (EU) Emissions Trading System (ETS) consistently promote renewable energy innovations. …