Digital Commons Network^™

Characterizing Meteorological Patterns For Oregon's Winter Elevated Pm2.5 Concentrations From 2000-2023, Arielle Golda Sherbak

Dissertations and Theses

Valleys and basins are uniquely susceptible to the buildup of air pollution during the wintertime due to cool, dense air pooling under favorable meteorological conditions, a common occurrence in the Intermountain West. In Oregon, wintertime anthropogenic air pollution is primarily from woodburning stoves and heaters and although efforts have been made to reduce wintertime pollution, stagnation events continue to result in decreased air quality. This analysis aims to understand the relationship between air pollution in six Oregon counties and the associated meteorology in the cool season (November-March) using a 2000-2023 climatology.

This is completed through a composite analysis using key …

Revisiting The Spin–Lattice Relaxation Of Homonuclear In-Phase And Antiphase Nmr Magnetization, Lauren M. Kehoe, Zachary G. Mayes, Kelsey E. Brakensiek, Emma L. Ellis, Adam J. Alderfer, Lingyu Chi, Klaus Woelk

Chemistry Faculty Research & Creative Works

Hyperpolarization techniques such as dynamic nuclear polarization (DNP), chemically induced dynamic nuclear polarization (CIDNP), and parahydrogen-induced polarization (PHIP) enhance the sensitivity of NMR spectroscopy and MRI, but the associated antiphase magnetization patterns often relax faster than those of conventional in-phase signals. This study analyzes the spin–lattice relaxation matrix for single-quantum transitions in an isolated, weakly coupled two-spin AX system to identify eigenvectors and eigenvalues that govern the time evolution of in-phase and antiphase longitudinal magnetization. The analysis predicts that AX antiphase magnetization, such as that generated by PHIP hydrogenations in high magnetic field, can relax up to twice as fast …

Fisheries Research Report No. 354: Recreational Fishing For Abalone In Western Australia In 2024/2025 - Estimates Of Participation, Effort And Catch, Claire B. Smallwood, Karina L. Ryan, Lachlan W. Strain

Fisheries Research Reports

The Western Australian Recreational Abalone Fishery (RAF) operates in shallow coastal waters as a hand collection fishery while wading or diving. Three species of abalone are allowed to be caught. Roe’s abalone (Haliotis roei) are generally collected while wading from the shore in accessible nearshore reef complexes along the West and South Coast Bioregions. Greenlip abalone (H. laevigata) and Brownlip abalone (H. conicopora) are collected while wading (including snorkelling and freediving) and diving (using compressed air) predominantly along the South Coast Bioregion.

Recreational fishing for the three abalone species is licensed and highly regulated, especially in the Western Zone (which …

Creativity In Large Language Models, Robert Kenneth Morain

Theses and Dissertations

As artificial intelligence systems approach human-level performance across a wide variety of domains, creative domains remain a critical yet underexplored area of research. Unlike traditional AI tasks with well-defined objectives, creative tasks rely on inherently subjective measures, making them particularly challenging for machines that lack human experiential context. This dissertation establishes the fundamental importance of creativity in AI systems and presents methods for improving and evaluating the creative capabilities of LLMs on the pathway toward AGI. This work addresses and examines key limitations in current LLM creativity through model innovations, development frameworks, and comparative studies. The dissertation develops novel architectural …

“Toward A World Where Many Worlds Fit”: Thinking-Feeling Our Way In The Pluriverse, Michael Burns

Sustainability Research & Practice Seminar Presentations

Dr. Michael Burns, English, shares "Toward a 'World Where Many Worlds Fit': Thinking-Feeling Our Way into the Pluriverse" Recording shares the lecture portion. Following the lecture participants shared their thoughts with one another.

Modeling Effective Shade For Oregon Streams Using Satellite Derived Vegetation Structure Metrics, Ella Wagner

Environmental Science and Management Professional Master's Project Reports

In the Pacific Northwest, rising stream temperatures threaten the survival of cold-water species, such as salmonids. The Oregon Department of Environmental Quality (DEQ) regulates stream temperature statewide through Total Maximum Daily Loads (TMDLs). To do this, the DEQ uses stream shade as a surrogate measurement for heat, as stream shade and stream temperature have a well-known strong relationship. This project focused on developing effective shade modeling and monitoring methods that increase the pace and scale of temperature TMDL implementation and tracking in Oregon. Stream shade can be measured in the field and modeled with computer models such as HeatSource, which …

Adipose Tissue Estrogen Receptor-Alpha Overexpression Ameliorates High-Fat Diet–Induced Adipose Tissue Inflammation, Marion C. Hope, Christian A. Unger, M. Chase Kettering, Cassidy E. Socia, Ahmed K. Aladhami, Barton C. Rice, Darya S. Niamira, Ben P. Wiznitzer, Diego Altomare Ph.D., William E. Cotham, Reilly T. Enos

Faculty Publications

Context

We created an innovative mouse model that enables inducible overexpression of estrogen receptor-alpha (ERα), specifically in adipose tissue (Adipo-ERα↑). Objective

We aimed to investigate how elevated Adipo-ERα↑ influences the development of high-fat diet (HFD)-induced obesity in both male and female mice. Methods

Male and female Adipo-ERα↑ mice and littermate controls were fed a low-fat diet (LFD) or HFD for 13 weeks. Adipo-ERα↑ was induced at the initiation of dietary treatment. Body morphology and composition, hepatic lipid accumulation, glucose tolerance, fasting insulin concentrations, and adipose tissue mRNA profiling were assessed. Liquid chromatography–mass spectrometry was used to determine circulating and adipose …

Pentanary And Hexanary Alkali Lanthanide Germanates Grown From Lanthanide Rich Flux Growth Reactions, Gregory Morrison, K. Pilar Zamorano, Virginia G. Jones, Ethan N. Adams, Hans Conrad Zur Loye

Faculty Publications

Single crystals of five compounds of a new structure type, the K3Nd52Ge12O100F7 type, were grown from lanthanide rich reactions using a KF/KCl flux: K3Ln52Ge12O100F7 (Ln = Nd, Eu), K3Ln52Ge12O100+δF7-δ (Ln = Pr, Tb), and K3Ca0.872Nd51.128Ge12O99.128F7.872. The five compounds crystallize in the cubic space group I-43d with lattice parameter 22.0350(2) Å for K3Nd52Ge12O100F7. The structure consists of a 3D checkerboard of LnX8, LnX7, LnX6, and GeO4 polyhedra, where X = O/F, and can be conceptually derived from the fluorite structure. K3Eu52Ge12O100F7 exhibits the fluorescence typical of Eu³⁺.

The Pastor As Romantic Author: Ai, Preaching, And The Unacknowledged Inheritance Of Authenticity, Daniel Plate, James Hutson

Faculty Scholarship

This article interrogates contemporary reactions to sermons produced with generative technologies through a historical–conceptual lens, arguing that widespread judgments of such outputs as “soulless,” “generic,” or lacking a “beating heart” are best explained by an unacknowledged inheritance from nineteenth-century Romantic expressivism. Rather than treating resistance to machine authorship as a theological verdict on computational incapacity, the study reconstructs how Romanticism centered authorship in sincere self-expression and solitary genius, displacing earlier heraldic expectations that prized fidelity to a received message. Methodologically, the analysis combines intellectual history with discourse analysis of global Christian experiments in synthetic composition (2020–2025), denominational guidance, and media …

Csc36000 - Modern Distributed Computing Assignment, Saptarashmi Bandyopadhyay

Open Educational Resources

This assignment covers standard performance metrics for Distributed Systems and the basics of Multiprocessing for CSC36000 - Modern Distributed Computing at the City College of New York CUNY. It is an interactive coding assignment intended to be executed in a Python notebook.

Delving Into Invisible Semantics For Generalized One-Shot Neural Human Rendering, Yihong Lin, Xuemiao Xu, Huaidong Zhang, Cheng Xu, Weijie Li, Yi Xie, Jing Qin, Shengfeng He

Research Collection School Of Computing and Information Systems

Traditional human neural radiance fields often overlook crucial body semantics, resulting in ambiguous reconstructions, particularly in occluded regions. To address this problem, we propose the Super-Semantic Disentangled Neural Renderer (SSD-NeRF), which employs rich regional semantic priors to enhance human rendering accuracy. This approach initiates with a Visible-Invisible Semantic Propagation module, ensuring coherent semantic assignment to occluded parts based on visible body segments. Furthermore, a Region-Wise Texture Propagation module independently extends textures from visible to occluded areas within semantic regions, thereby avoiding irrelevant texture mixtures and preserving semantic consistency. Additionally, a view-aware curricular learning approach is integrated to bolster the model's …

Stroke2sketch: Harnessing Stroke Attributes For Training-Free Sketch Generation, Rui Yang, Huining Li, Yiyi Long, Xiaojun Wu, Shengfeng He

Research Collection School Of Computing and Information Systems

Generating sketches guided by reference styles requires precise transfer of stroke attributes, such as line thickness, deformation, and texture sparsity, while preserving semantic structure and content fidelity. To this end, we propose Stroke2Sketch, a novel training-free framework that introduces cross-image stroke attention, a mechanism embedded within self-attention layers to establish fine-grained semantic correspondences and enable accurate stroke attribute transfer. This allows our method to adaptively integrate reference stroke characteristics into content images while maintaining structural integrity. Additionally, we develop adaptive contrast enhancement and semanticfocused attention to reinforce content preservation and foreground emphasis. Stroke2Sketch effectively synthesizes stylistically faithful sketches that closely …

Cross-Subject Mind Decoding From Inaccurate Representations, Yangyang Xu, Bangzhen Liu, Wenqi Shao, Yong Du, Shengfeng He, Tingting Zhu

Research Collection School Of Computing and Information Systems

Decoding stimulus images from fMRI signals has advanced with pre-trained generative models. However, existing methods struggle with cross-subject mappings due to cognitive variability and subject-specific differences. This challenge arises from sequential errors, where unidirectional mappings generate partially inaccurate representations that, when fed into diffusion models, accumulate errors and degrade reconstruction fidelity. To address this, we propose the Bidirectional Autoencoder Intertwining framework for accurate decoded representation prediction. Our approach unifies multiple subjects through a Subject Bias Modulation Module while leveraging bidirectional mapping to better capture data distributions for precise representation prediction. To further enhance fidelity when decoding representations into stimulus images, …

Classical Shadows With Improved Median-Of-Means Estimation, Winston Fu, Dax Enshan Koh, Siong Thye Goh, Jian Feng Kong

Research Collection School Of Computing and Information Systems

The classical shadows protocol, introduced by Huang et al (2020 Nat. Phys. 16 1050), makes use of the median-of-means (MoM) estimator to efficiently estimate the expectation values of M observables with failure probability δ using only O ( log ⁡ ( M / δ ) ) measurements. In their analysis, Huang et al used loose constants in their asymptotic performance bounds for simplicity. However, the specific values of these constants can significantly affect the number of shots used in practical implementations. To address this, we studied a modified MoM estimator proposed by Minsker (2023 Proc. 36th Conf. on Learning Theory …

Deep Learning For Hate Speech Detection: A Comparative Study, Jitendra Singh Malik, Hezhe Qiao, Guansong Pang, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

Automated hate speech detection is an important tool in combating the spread of hate speech, particularly in social media. Numerous methods have been developed for the task, including a recent proliferation of deep-learning based approaches. A variety of datasets have also been developed, exemplifying various manifestations of the hate-speech detection problem. We present here a largescale empirical comparison of deep and shallow hate-speech detection methods, mediated through the three most commonly used datasets. Our goal is to illuminate progress in the area, and identify strengths and weaknesses in the current state-of-the-art. We particularly focus our analysis on measures of practical …

Teaching Diffusion Models To Ground Alpha Matte, Tianyi Xiang, Weiying Zheng, Yutao Jiang, Tingrui Shen, Hewei Yu, Yangyang Xu, Shengfeng He

Research Collection School Of Computing and Information Systems

The power of visual language models is showcased in visual understanding tasks, where language-guided models achieve impressive flexibility and precision. In this paper, we ex tend this capability to the challenging domain of image matting by framing it as a soft grounding problem, enabling a single diffusion model to handle diverse objects, textures, and transparencies, all directed by descriptive text prompts. Our method teaches the diffusion model to ground alpha mattes by guiding it through a process of instance-level localization and transparency estimation. First, we introduce an intermediate objective that trains the model to accurately localize semantic components of the …

Stable Score Distillation, Haiming Zhu, Yangyang Xu, Chenshu Xu, Tingrui Shen, Wenxi Liu, Yong Du, Jun Yu, Shengfeng He

Research Collection School Of Computing and Information Systems

Text-guided image and 3D editing have advanced with diffusion-based models, yet methods like Delta Denoising Score often struggle with stability, spatial control, and editing strength. These limitations stem from reliance on complex auxiliary structures, which introduce conflicting optimization signals and restrict precise, localized edits. We introduce Stable Score Distillation (SSD), a streamlined framework that enhances stability and alignment in the editing process by anchoring a single classifier to the source prompt. Specifically, SSD utilizes Classifier-Free Guidance (CFG) equation to achieve cross-prompt alignment, and introduces a constant term null-text branch to stabilize the optimization process. This approach preserves the original content's …

Tactile Data Comics: Combining Step-By-Step Presentation Of Tactile Graphics With Verbal Narration For The Blind And Visually Impaired, Yang Jiao, Ruoting Sun, Rong Luo, Xiwen Yao, Xinran She, Kotaro Hara, Yuewen Zhang, Xinyi Fu

Research Collection School Of Computing and Information Systems

Tactile graphics on a refreshable display have proven effective in enabling visually impaired people to comprehend pictorial content. To further evaluate the effectiveness of refreshable tactile displays in blind education, we designed tactile data comics, a method that combines step-by-step presentation of tactile graphics with verbal narration. We conducted a user study with sixteen visually impaired students to compare tactile data comics against verbal-only and static tactile graphics. Our findings show that tactile data comics significantly improve participants’ comprehension and engagement during the learning experience. These empirical results suggest that the integration of refreshable tactile displays and tactile data comics …

Impact Of Original Versus Reposted Social Endorsements On Content Consumption: The Moderating Role Of Endorsers’ Network Characteristics, Anqi Zhao, Qian Tang

Research Collection School Of Computing and Information Systems

Social endorsements broadcast endorsers’ positive attitudes toward content or products, especially to their social ties. Original endorsements created by endorsers can be propagated further as reposted endorsements. Both are important marketing tools to increase content consumption, yet their differences are unclear. This study compares the impacts of original and reposted endorsements on content consumption and their contingencies on the endorsers’ network characteristics. Using data on social endorsements of YouTube videos on Twitter, we find that original endorsements (i.e., original tweets) significantly boost content consumption, and the effect is positively moderated by the endorsers’ network size but not their tie strength. …

Integrating Satellite Observations And Modeling Approaches To Study Tectonic And Land Surface Hazards, Jeng Hann Chong

Earth and Planetary Sciences ETDs

The increasing availability of Earth observation satellites, such as SAR and optical platforms, has improved our ability to study the ground deformation processes during, after, and leading up to these natural hazards, such as earthquakes and debris flows. In this dissertation, I analyze satellite geodetic interseismic velocities across the Rakhine-Bangladesh megathrust and the influence of having realistic modeling assumptions for subduction zones to better constrain seismic hazard. I also examined changes in post-fire debris flow likelihood four years after the 2020 Lake Fire in Southern California using a suite of satellite data and field observations. Together, these studies highlight how …

(Si15-010) On Perturbations Of Gabor Frames, Jamkhongam Touthang

Applications and Applied Mathematics: An International Journal (AAM)

Stability plays a crucial role in frame theory and its applications. The present paper studies the interaction between Gabor frames and perturbations, presenting perturbation results related to small changes of the frame parameters and the window functions both in regular and irregular settings. Examples are provided for illustration. Additionally, the paper briefly discusses algorithms pertinent to Gabor frames under perturbations and highlights challenging areas in the field.

The Complexity Of Long-Distance Dependencies And Their Impact On Language Models, Abhijit Shrikant Mahalunkar

Doctoral

Sequential data modeling is an important challenge in various fields and in particular in natural language processing. Building effective sequential models faces a notable challenge in the form of Long-Distance Dependencies (LDDs) within the sequence data. Hence, successfully overcoming this challenge is imperative for developing robust and accurate sequential models across various domains and applications. To tackle this challenge, the first step is to conduct a detailed analysis of the complexity of LDDs observed in various sequence datasets. This thesis offers a thorough exploration and documentation of this analysis. An important finding from this thesis is the consistent patterns of …

Analysis Of The Status And Thematic Trends Of Ai For Science Research Abroad From 2015 To 2024, Fangyuan Wang, Huiting Xu, Jinghua Xue

Journal of Scientific Information Research

[Purpose/significance] This paper analyzes the relevant literature in the field of AI for Science（AI4S）in the WoS core database from 2015 to 2024, and sorts out the research status and development trends in this field, aiming to provide forward-looking insights for the application of AI technology in scientific research.

[Method/process] This paper combines bibliometric analysis with the BERTopic model to analyze the publication trends, publishing countries, core authors, and topic identification and development trends in the field of AI4S.

[Result/conclusion] Through bibliometric analysis, this paper reveals the exponential growth trend of AI4S-related literature, and finds that China ranks first in the …

Dcrda: Deadline-Constrained Function Scheduling In Serverless-Cloud Platform, Priyanka Ashok Birajdar, Divya Meena, Anurag Satpathy, Sourav Kanti Addya

Computer Science Faculty Research & Creative Works

The serverless computing model frees developers from operational and management tasks, allowing them to focus solely on business logic. This paper addresses the computationally challenging function-container-virtual machine (VM) scheduling problem, especially under stringent deadline constraints. We propose a two-stage holistic scheduling framework called DCRDA targeting deadline-constrained function scheduling. In the first stage, the function-to-container scheduling is modeled as a one-to-one matching game and solved using the classical Deferred Acceptance Algorithm (DAA). The second stage addresses the container-to-VM assignment, modeled as a many-to-one matching problem, and solved using a variant of the DAA, the Revised-Deferred Acceptance Algorithm (RDA), to account for …

The Fate Of Recycled Water In Restored Wetlands, Punyotoya Paul

Environmental Science and Management Professional Master's Project Reports

This research investigates the hydrological fate of treated wastewater or "recycled water" used for irrigation at a restored wetland site (Thomas Dairy) near Tigard, Oregon, that is managed by Clean Water Services (CWS). CWS is interested in understanding how treated wastewater moves through plants and soil in ecosystems that might be targeted for recycled water applications. Using a water balance approach, we evaluated irrigation inputs, soil infiltration dynamics, and hydraulic conductivity measurements across three soil types with different textures and other properties. Field measurements were collected using advanced sensors (TEROS and ATMOS devices) and laboratory tools (KSAT) to quantify soil …

Honors Faculty Research Talk Ml Tlachac, Ml Tlachac

Honors Programs

PowerPoint Slides of Honors Research Talk by ML Tlachac.

A Comprehensive Review Of Financial Knowledge Graphs, Jeyaraman Brindha Priyadarshini, Bing Tian Dai, Yuan Fang

Research Collection School Of Computing and Information Systems

Knowledge Graphs (KGs) are increasingly used in finance to manage complex, interconnected data and support advanced analytics. This survey provides an overview of how KGs are applied across various financial areas, such as fraud detection, credit risk assessment, anti-money laundering, and regulatory compliance. We examine key techniques for building and using KGs in finance, including graph construction, embedding methods, and machine learning models. The survey also discusses challenges specific to finance, like handling private data, ensuring interpretability, and managing real-time data. Additionally, we explore the emerging combination of KGs with large language models and generative AI, which offers new possibilities …

Filterfl: Knowledge Filtering-Based Data-Free Backdoor Defense For Federated Learning, Yanxin Yang, Ming Hu, Xiaofei Xie, Yue Cao, Pengyu Zhang, Yihao Huang, Mingsong Chen

Research Collection School Of Computing and Information Systems

As a distributed machine learning paradigm, Federated Learning (FL) enables large-scale clients to collaboratively train a model without sharing their raw data. However, due to the lack of data auditing for untrusted clients, FL is vulnerable to poisoning attacks, especially backdoor attacks. By using poisoned data for local training or directly changing the model parameters, attackers can easily inject backdoors into the model, which can trigger the model to make misclassification of targeted patterns in images. To address these issues, we propose a novel data-free trigger-generation-based defense approach based on the two characteristics of backdoor attacks: i) triggers are learned …

Seeing 3d Through 2d Lenses: 3d Few-Shot Class-Incremental Learning Via Cross-Modal Geometric Rectification, Tuo Xiang, Xuemiao Xu, Bangzhen Liu, Jinyi Li, Yong Li, Shengfeng He

Research Collection School Of Computing and Information Systems

The rapid growth of 3D digital content necessitates expandable recognition systems for open-world scenarios. However, existing 3D class-incremental learning methods struggle under extreme data scarcity due to geometric misalignment and texture bias. While recent approaches integrate 3D data with 2D foundation models (e.g., CLIP), they suffer from semantic blurring caused by texture-biased projections and indiscriminate fusion of geometric-textural cues, leading to unstable decision prototypes and catastrophic forgetting. To address these issues, we propose Cross-Modal Geometric Rectification (CMGR), a framework that enhances 3D geometric fidelity by leveraging CLIP’s hierarchical spatial semantics. Specifically, we introduce a Structure-Aware Geometric Rectification module that hierarchically …

Omnivton: Training-Free Universal Virtual Try-On, Zhaotong Yang, Yuhui Li, Shengfeng He, Xinzhe Li, Yangyang Xu, Junyu Dong, Yong Du

Research Collection School Of Computing and Information Systems

Image-based Virtual Try-On (VTON) techniques rely on either supervised in-shop approaches, which ensure high fidelity but struggle with cross-domain generalization, or unsupervised in-the-wild methods, which improve adaptability but remain constrained by data biases and limited universality. A unified, training-free solution that works across both scenarios remains an open challenge. We propose OmniVTON, the first training-free universal VTON framework that decouples garment and pose conditioning to achieve both texture fidelity and pose consistency across diverse settings. To preserve garment details, we introduce a garment prior generation mechanism that aligns clothing with the body, followed by continuous boundary stitching technique to achieve …