Digital Commons Network^™

College Of Saint Benedict And Saint John's University Fy2023 Greenhouse Gas Emissions Inventory, Csb+Sju Office Of Sustainability

Sustainability Office Publications

The College of Saint Benedict (CSB) and Saint John’s University (SJU) Office of Sustainability conducted a comprehensive Greenhouse Gas (GHG) Inventory that includes emissions from both CSB and SJU campuses. This report details GHG emissions for Fiscal Year (FY) 2023 (July 1, 2022-June 30, 2023) in Metric Tons of Carbon Dioxide Equivalents (MTCO2e), the standard unit of measurement used to track and report GHG emissions based on their global warming potential. Data was collected, organized, and analyzed by staff in the CSB and SJU Sustainability Office.

Why Empirical Membership Functions Are Well-Approximated By Piecewise Quadratic Functions: Theoretical Explanation For Empirical Formulas Of Novak's Fuzzy Natural Logic, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Empirical analysis shows that membership functions describing expert opinions have a shape that is well described by a smooth combination of two quadratic segments. In this paper, we provide a theoretical explanation for this empirical phenomenon.

Creative Connections: Building Empathy To Foster Ecoliteracy Through Art Education, Jocelyn Salim

Masters Theses

This thesis investigates the potential positive impact of fostering empathy and understanding for the natural world through art education. Through action research, this study examines various teaching approaches, such as incorporating scientific knowledge, employing literature to discuss ecological themes, and engaging in participatory storytelling activities to cultivate empathy among elementary school children. The objective of this thesis is to explore empathy as a potential pathway to encourage children to foster connections with the natural world and develop compassionate traits, attitudes, and behaviors towards nature as they grow. The findings of this study reveal that children exhibit high levels of enthusiasm …

Differential Methylation Region Detection Via An Array-Adaptive Normalized Kernelweighted Model, Daniel Alhassan, Gayla R. Olbricht, Akim Adekpedjou

Mathematics and Statistics Faculty Research & Creative Works

A differentially methylated region (DMR) is a genomic region that has significantly different methylation patterns between biological conditions. Identifying DMRs between different biological conditions is critical for developing disease biomarkers. Although methods for detecting DMRs in microarray data have been introduced, developing methods with high precision, recall, and accuracy in determining the true length of DMRs remains a challenge. In this study, we propose a normalized kernel-weighted model to account for similar methylation profiles using the relative probe distance from "nearby" CpG sites. We also extend this model by proposing an array-adaptive version in attempt to account for the differences …

Herbicides, Department Of Primary Industries And Regional Development, Western Australia

Grains and other field crops factsheets

Herbicides play a vital role in integrated weed management programs. Knowledge of the mechanisms and activity of herbicides will improve the impact and sustainability of herbicides as a weed management tactic. This fact sheet is a guide to the types of herbicides available and when and how they should be used.

Quasi-Monte Carlo Estimation For Functional Generalized Linear Mixed Models., Ruvini Kumari Jayamaha Hitihamilage

Dissertations

Functional Data Analysis (FDA) is a topic of growing interest in the statistics community and is applied in a wide range of fields such as Anthropology, Epidemiology, Meteorology, Neurology and Engineering. The data in FDA are smooth curves or surfaces in time or space which can be conceptualized as functions. Because of the smooth nature of the data and the measurements are highly correlated, making the classical methods such as univariate or multivariate analysis are infeasible for such data. Functional data Analysis (FDA) deals with these kinds of more detailed, complex, and structured data.

In this dissertation, we propose a …

Effects Of Differing Radiation Methods On Charge Transport In Polymers, Zachary J. Gibson, Jr Dennison, Virginie Griseri

Conference Proceedings

Spacecraft charging issues are understood and mitigated through an understanding of material properties. Material properties are dynamic in the harsh environment of space. Approximations must be made to simulate the space environment in the laboratory. This paper reports on the investigation of the approximation that energy deposition causes the same aging effects in the materials regardless of the radiation source. Samples of polytetrafluoroethylene (PTFE) and polyether-etherketone (PEEK) were irradiated with x-rays, γ-rays, or electrons at total ionizing dose (TID) of either 2 x 10⁴, 2 x 10⁵, or 2 x 10⁶ rad. Charge was then …

Combinatorial Creativity: Knowledge Graphs And Idea Generation In Crowdsourcing Innovation, Zhi Wei Vincent Mack

Dissertations and Theses Collection (Open Access)

This dissertation explores the dynamic interplay between combinatorial creativity and technology-driven innovation within various knowledge-intensive fields. It critically examines the role of combinatorial creativity in generating groundbreaking innovations by amalgamating existing ideas and technologies. This research incorporates a detailed examination of how knowledge, whether tacit or explicit, can be transformed into actionable data to foster innovation in crowdsourcing contexts. Chapter 2 provides an overview of the relevant literature on how Artificial Intelligence and Knowledge Management Systems can support combinatorial creativity. The study further delves into the transformative impact of knowledge management systems, particularly focusing on crowdsourcing platforms that leverage collective …

Design And Implementation Of A Vision-Based Deep-Learning Protocol For Kinematic Feature Extraction With Application To Stroke Rehabilitation, Juan Diego Luna Inga

Master's Theses

Stroke is a leading cause of long-term disability, affecting thousands of individuals annually and significantly impairing their mobility, independence, and quality of life. Traditional methods for assessing motor impairments are often costly and invasive, creating substantial barriers to effective rehabilitation. This thesis explores the use of DeepLabCut (DLC), a deep-learning-based pose estimation tool, to extract clinically meaningful kinematic features from video data of stroke survivors with upper-extremity (UE) impairments.

To conduct this investigation, a specialized protocol was developed to tailor DLC for analyzing movements characteristic of UE impairments in stroke survivors. This protocol was validated through comparative analysis using peak …

Navigating The Ethical Terrain Of Ai In Higher Education: Strategies For Mitigating Bias And Promoting Fairness, Emily Barnes, James Hutson

Faculty Scholarship

Artificial intelligence (AI) and machine learning (ML) are transforming higher education by enhancing personalized learning and academic support, yet they pose significant ethical challenges, particularly in terms of inherent biases. This review critically examines the integration of AI in higher education, underscoring the dual aspects of its potential to innovate educational paradigms and the essential need to address ethical implications to avoid perpetuating existing inequalities. The researchers employed a methodological approach that analyzed case studies and literature as primary data collection methods, focusing on strategies to mitigate biases through technical solutions, diverse datasets, and strict adherence to ethical guidelines. Their …

Communication Challenges In Underwater Wireless Networks: Mac Protocols And Software Solutions, Dmitrii Dugaev

Dissertations, Theses, and Capstone Projects

Underwater wireless networks (UWNs) represent a diverse and intriguing research domain, encompassing a wide array of scientific and industrial applications. This dissertation delves into the communication challenges at the Medium Access Control (MAC) layer within UWNs, stemming from the distinctive signal propagation conditions and the harshness of the deployment environment. The manuscript provides comprehensive coverage of key aspects of UWNs, including potential applications, communication protocols, methodologies employed in such networks, and existing software solutions that facilitate simulation, emulation, and real testbed scenarios for underwater research endeavors. Furthermore, this research introduces innovative software and communication solutions designed to facilitate the seamless …

Moral Responsibility And Other Visceral Attitudes Of Western Michigan University Students Toward Food Waste, Quinn Heiser

Masters Theses

Despite the large number of people that go hungry on a regular basis in America, food waste remains a looming issue, globally and nationally. Both American college students and the universities themselves contribute a large portion to the national food waste totals. A dichotomy that is especially curious is the difference between students who live on-campus and whose main source of meals is the dining centers, versus students who live off campus and rely mainly on groceries to make meals independently. However, food waste has seldom been studied in the context of the fact that college is a major transition …

Drag Your Noise: Interactive Point-Based Editing Via Diffusion Semantic Propagation, Haofeng Liu, Chenshu Xu, Yifei Yang, Lihua Zeng, Shengfeng He

Research Collection School Of Computing and Information Systems

Point-based interactive editing serves as an essential tool to complement the controllability of existing generative models. A concurrent work, DragDiffusion, updates the diffusion latent map in response to user inputs, causing global latent map alterations. This results in imprecise preservation of the original content and unsuccessful editing due to gradient vanishing. In contrast, we present DragNoise, offering robust and accelerated editing without retracing the latent map. The core rationale of DragNoise lies in utilizing the predicted noise output of each U-Net as a semantic editor. This approach is grounded in two critical observations: firstly, the bottleneck features of U-Net inherently …

Diffusion Time-Step Curriculum For One Image To 3d Generation, Xuanyu Yi, Zike Wu, Qingshan Xu, Pan Zhou, Joo Hwee Lim, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Score distillation sampling (SDS) has been widely adopted to overcome the absence of unseen views in reconstructing 3D objects from a single image. It leverages pretrained 2D diffusion models as teacher to guide the reconstruction of student 3D models. Despite their remarkable success, SDS-based methods often encounter geometric artifacts and texture saturation. We find out the crux is the overlooked indiscriminate treatment of diffusion time-steps during optimization: it unreasonably treats the studentteacher knowledge distillation to be equal at all time-steps and thus entangles coarse-grained and fine-grained modeling. Therefore, we propose the Diffusion Time-step Curriculum one-image-to-3D pipeline (DTC123), which involves both …

Present Case Studies Highlighting Practical Implications Of Architectural Design Choices, Emily Barnes, James Hutson

Faculty Scholarship

The interpretability of deep neural networks (DNNs) has become a crucial focus within artificial intelligence and machine learning, particularly as these models are increasingly used in high-stakes applications such as healthcare, finance, and autonomous driving. This article explores the impact of architectural design choices on the interpretability of DNNs, emphasizing the importance of transparency, trust, and accountability in AI systems. By presenting case studies and experimental results, the article highlights how different architectural elements—such as layer types, network depth, connectivity patterns, and attention mechanisms—affect model interpretability and performance. The discussion is structured into three main sections: real-world applications, architectural trade-offs, …

Snpaimer: R Package For Evaluating Ancestry Informative Marker Contributions In Non-Model Population Diagnostics, Kim L. Vertacnik, Oksana V. Vernygora, Julian R. Dupuis

Entomology Faculty Publications

Motivation: Single nucleotide polymorphism (SNP) markers are increasingly popular for population genomics and inferring ancestry for individuals of unknown origin. Because large SNP datasets are impractical for rapid and routine analysis, diagnostics rely on panels of highly informative markers. Strategies exist for selecting these markers, however, resources for efficiently evaluating their performance are limited for non-model systems.

Results: snpAIMeR is a user-friendly R package that evaluates the efficacy of genomic markers for the cluster assignment of unknown individuals. It is intended to help minimize panel size and genotyping effort by determining the informativeness of candidate diagnostic markers. Provided genotype data …

Using Bayesian Multispecies Models To Evaluate Fish And Invertebrate Detection Probability And Distribution In The Hypersaline Bahia Grande Tidal Basin, Roy M. Ulibarri, Catherine M. Eckert, David Hicks, David Montagne, Brandon Jones, David R. Stewart

School of Earth, Environmental, & Marine Sciences Faculty Publications

Objective

In 2000, the Laguna Atascosa National Wildlife Refuge acquired the Bahia Grande (Texas) management unit, a space that had lain barren and arid for 70 years. A large cooperative partnership launched a restoration project to replenish the basin and recover its original tidal hydrology. In 2005, the construction of a pilot channel successfully restored water throughout the basin, and plans to eventually widen the channel were developed. Our study aims to evaluate an estuarine habitat restoration by assessing ecological drivers and the impacts on species diversity.

Methods

We evaluated species richness, detection/occupancy rates, and species–habitat relationships, and we estimated …

Grazing Annual Pastures – Using Feed On Offer (Foo) As A Guide, Department Of Primary Industries And Regional Development, Western Australia

Animal production and livestock factsheets

Grazing management in winter and spring in South West Western Australia (SW WA) can manipulate the quality, quantity and composition of pasture. These pasture changes can increase livestock feed efficiency and productivity, reduce insect populations and improve pasture persistence. Seasonal grazing based on feed on offer (FOO) is described here to maximise stocking rate and pasture production.

The Department of Primary Industries and Regional Development recommends using feed measurements and feed budgeting to increase productivity and reduce livestock health risks.

Boosting Winter Pasture Growth, Department Of Primary Industries And Regional Development, Western Australia

Grains and other field crops factsheets

Applying gibberellic acid or nitrogen fertiliser to pasture in winter can increase dry matter production of grasses and broadleaf weeds and help reduce feed shortages in winter.

Consistent3d: Towards Consistent High-Fidelity Text-To-3d Generation With Deterministic Sampling Prior, Zike Wu, Pan Zhou, Xuanyu Yi, Xiaoding Yuan, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Score distillation sampling (SDS) and its variants have greatly boosted the development of text-to-3D generation, but are vulnerable to geometry collapse and poor textures yet. To solve this issue, we first deeply analyze the SDS and find that its distillation sampling process indeed corresponds to the trajectory sampling of a stochastic differential equation (SDE): SDS samples along an SDE trajectory to yield a less noisy sample which then serves as a guidance to optimize a 3D model. However, the randomness in SDE sampling often leads to a diverse and unpredictable sample which is not always less noisy, and thus is …

Open-Vocabulary Video Anomaly Detection, Peng Wu, Xuerong Zhou, Guansong Pang, Yujia Sun, Jing Liu, Peng Wang, Yanning Zhang

Research Collection School Of Computing and Information Systems

Current video anomaly detection (VAD) approaches with weak supervisions are inherently limited to a closed-set setting and may struggle in open-world applications where there can be anomaly categories in the test data unseen during training. A few recent studies attempt to tackle a more realistic setting, open-set VAD, which aims to de-tect unseen anomalies given seen anomalies and normal videos. However, such a setting focuses on predicting frame anomaly scores, having no ability to recognize the specific categories of anomalies, despite the fact that this ability is essential for building more informed video surveillance systems. This paper takes a step …

Impact Of Similarities In Gender And Physical Appearance Between User And Embodied Conversational Agents On Trustworthiness, Empathy, And Service Evaluation, Sookyoung Park

Dartmouth College Master’s Theses

Embodied conversational agents (ECAs) have significantly enhanced human-machine interactions and show considerable potential in various industries such as customer service, education, healthcare, entertainment, and finance [1, 2]. This study explores the impact of similarities in gender and physical appearance between ECAs and users on the perceptions of trustworthiness, empathy, and service evaluation within the context of counselor ECAs. We conducted a within-subject experiment (n=50), using a 2x2 factorial arrangement, that varied the gender and the physical appearance of four distinct AI avatars. Participants interacted with each avatar, completing a post-experiment survey and participating in semi-structured interviews. Our findings indicate that …

Catalytic Role Of Histidine-114 In The Hydrolytic Dehalogenation Of Chlorothalonil By Pseudomonas Sp. Ctn-3, Grayson Gerlich, Callie Miller, Xinhang Yang, Brian Bennett, Judith Klein-Seetharaman, Richard C. Holz

Physics Faculty Research and Publications

Chlorothalonil (2,4,5,6-tetrachloroisophthalonitrile; TPN) is an environmentally persistent fungicide that sees heavy use in the USA and is highly toxic to aquatic species and birds, as well as a probable human carcinogen. The chlorothalonil dehalogenase from Pseudomonas sp. CTN-3 (Chd, UniProtKB C9EBR5) degrades TPN to its less toxic 4-OH-TPN analog making it an exciting candidate for the development of a bioremediation process for TPN; however, little is currently known about its catalytic mechanism. Therefore, an active site residue histidine-114 (His114) which forms a hydrogen bond with the Zn(II)-bound water/hydroxide and has been suggested to be the active site acid/base, was substituted …

Balancing Darkness And Visibility: An Algorithmic Approach To Light Placement In Low-Light, Ray-Traced Scenes, Briana Kuo

Master's Theses

In recent years, digital media has seen incredible advancements in rendering visually stunning computer graphics scenes. Photo-realistic games, animated films, and more leave viewers blown away by the sheer beauty of their graphics. However, challenges arise when depicting dark scenes, often resulting in visual monotony and difficulty in comprehension due to insufficient detail within the scene. In order to enhance readability and visual interest of a scene, additional, artificial lights can be placed throughout a scene to enhance the aesthetic. These lights, however, must be strategically placed in order to retain an essence of darkness and maintain the delicate balance …

Morp: Monocular Orientation Regression Pipeline, Jacob Gunderson

Master's Theses

Orientation estimation of objects plays a pivotal role in robotics, self-driving cars, and augmented reality. Beyond mere position, accurately determining the orientation of objects is essential for constructing precise models of the physical world. While 2D object detection has made significant strides, the field of orientation estimation still faces several challenges. Our research addresses these hurdles by proposing an efficient pipeline which facilitates rapid creation of labeled training data and enables direct regression of object orientation from a single image. We start by creating a digital twin of a physical object using an iPhone, followed by generating synthetic images using …

Using Plankton Edna To Estimate Whale Abundances Off The California Coast: Data Integration And Statistical Modeling, Katherine Chan

Master's Theses

Understanding marine mammal populations and how they are affected by human activity and ocean conditions is vital, especially in tracking population declines and monitoring endangered species. However, tracking marine mammal populations and their distribution is challenging due to difficulties in observation and costs. Using surrounding plankton environmental DNA (eDNA) has the potential to provide an indirect measure of monitoring cetacean abundances based on ecological associations. This project aims to apply statistical methods to assess the relationship of visual abundances of common species of baleen whales with amplicon sequence variants (ASV) of plankton eDNA samples from the NOAA-CalCOFI Ocean Genomics (NCOG) …

Searching For The Hyperobject: Crystals As Transscalar Vehicles, Jay Costello

Masters Theses

When I touch the street outside my house, I'm touching Los Angeles—a contiguous vector of material bisecting a continent. A slab, a stone, dust and oil, a googolplex of tightly packed anisotropic particles... At nightfall, I sneak to the edge of the highway and break off a piece. 'What does it mean for a worm to be aware of the scale of the planet?' Bruno Latour's evocative questioning of scalar jumps prompts an existentialism that places me somewhere between the hyperlocal and the massively distributed. Like a cosmic traveler floating through the universe, I feel adrift. I look around, grasping …

A Thesis, Or Digressions On Sculptural Practice: In Which, Concepts & Influences Thereof Are Explained, Set Forth, Catalogued, Or Divulged By Way Of Commentaries To A Poem, First Conceived By The Artist, Fed Through Chatg.P.T., And Re-Edited By The Artist, To Which Are Added, Annotated References, Impressions And Ruminations Thereof, Also Including Private Thoughts & Personal Accounts Of The Artist, Jaimie An

Masters Theses

This thesis is an exercise in, perhaps a futile, attempt to trace just some of the ideas, stories, and musings I might meander through in my process. It’s not quite a map, nor is it a neat catalogue; it is a haphazard collection of tickets and receipts from a travel abroad, carelessly tossed in a carry-on, only to be stashed upon returning home. These ideas are derived from much greater thinkers and authors than myself; I am a mere collector or a translator, if that, and not a very good one, for much is lost. I do not claim comprehensive …

Jollygesture: Exploring Dual-Purpose Gestures In Vr Presentations, Gun Woo Warren Park, Anthony Tang, Fanny Chevalier

Research Collection School Of Computing and Information Systems

Virtual reality (VR) offers new opportunities for presenters to use expressive body language to engage their audience. Yet, most VR presentation systems have adopted control mechanisms that mimic those found in face-to-face presentation systems. We explore the use of gestures that have dual-purpose: first, for the audience, a communicative purpose; second, for the presenter, a control purpose to alter content in slides. To support presenters, we provide guidance on what gestures are available and their effects. We realize our design approach in JollyGesture, a VR technology probe that recognizes dual-purpose gestures in a presentation scenario. We evaluate our approach through …

Learning Transferable Negative Prompts For Out-Of-Distribution Detection, Tianqi Li, Guansong Pang, Xiao Bai, Wenjun Miao, Jin Zheng

Research Collection School Of Computing and Information Systems

Existing prompt learning methods have shown certain capabilities in Out-of-Distribution (OOD) detection, but the lack of OOD images in the target dataset in their training can lead to mismatches between OOD images and In-Distribution (ID) categories, resulting in a high false positive rate. To address this issue, we introduce a novel OOD detection method, named ‘NegPrompt’, to learn a set of negative prompts, each representing a negative connotation of a given class label, for delineating the boundaries between ID and OOD images. It learns such negative prompts with ID data only, without any reliance on external out-lier data. Further, current …