Digital Commons Network^™

2024 Oregon Statewide Homelessness Estimates, Franklin H. Spurbeck, Jacen Greene, Marisa Zapata

Homelessness Research & Action Collaborative Publications and Presentations

The Point-in-Time (PIT) Count is a census of people experiencing both sheltered and unsheltered homelessness on a single night in January. The federal government requires this at least every other year as a condition of funding it distributes to Continuums of Care (CoCs), networks of government agencies and service providers that manage homelessness services and funding in specific regions. The unsheltered PIT Count is only required during odd-numbered years. Because of the cost and complexity involved, not all CoCs conduct an unsheltered count in even-numbered years. In 2024, all Oregon CoCs submitted sheltered PIT Count data. Only some conducted an …

Forging Common Ground In Second Language Acquisition And Teaching: A Combined Synergy Statement, Marije Michel, Dwight Atkinson, Amable Custodio Ribeiro, Theodora Alexopoulou, Marco Cappellini, Søren Wind Eskildsen, Xuesong (Andy) Gao, John Hellermann, Hayriye Kayi‐Aydar, Steven L. Thorne, Multiple Additional Authors

World Languages and Literatures Faculty Publications and Presentations

In this final article in this guest‐edited issue on synergies in second language acquisition and teaching (SLA/T), we attempt to bring together the main content of this issue in an overall, combined synergy statement, concluding this project as a whole. Let us remind readers of the project: A shared effort by 17 scholars taking 11 different perspectives on SLA/T is not the norm in our field. Our aim has been to promote the intermingling and cross‐pollination of ideas, on the conviction that such efforts are needed to promote a field that is more than simply the sum of its parts, …

Bulletin: General Catalog Issue 2025-2026, Portland State University

Bulletin & Curriculum Change Documents

No abstract provided.

Recovering Population Of The Southern Sea Otter Suppresses A Global Marine Invader, Rikke Jeppesen, Catherine E. De Rivera, Edwin D. Grosholz, M. Tim Tinker, Brent B. Hughes, Ron Eby, Kerstin Wasson

Environmental Science and Management Faculty Publications and Presentations

Understanding the role of apex predators on ecosystems is essential for designing effective conservation strategies. Supporting recovery of apex predators can have many benefits; one that has been rarely examined is control of invasive prey. We investigated whether a recovering apex predator, the southern sea otter (Enhydra lutris nereis), can exert local control over a global marine invader, the green crab (Carcinus maenas). We determined that southern sea otters in Elkhorn Slough estuary in California can consume large numbers of invasive green crabs and found strong negative relationships in space and time between otter and green crab abundance. Green crabs …

Collaborative Wildfire Governance Within The Southwest Idaho Wildfire Crisis Strategy Landscape: Survey Results 2025, Mahmood Muttaqee, Cody R. Evers, Lindsay K. Campbell, Max Nielsen-Pincus, Olivia Awbrey, Kathryn Hixson, Michelle Johnson, Erika Svendsen

Environmental Science and Management Faculty Publications and Presentations

Collaboration plays a critical role in wildfire risk mitigation efforts. This study examines collaborative wildfire governance networks within Southwest Idaho, a priority landscape designated in 2022 under the USDA Forest Service's Wildfire Crisis Strategy (WCS). Through survey data from 64 respondents, the Fueling Adaptation study aimed to identify the structure, capacity, and gaps in governance networks addressing wildfire risk in Southwest Idaho and understand how WCS investments have influenced wildfire management in the area. The findings reveal a growing governance network centered on the USDA Forest Service and the Idaho Department of Lands and supported by numerous non-governmental organizations and …

Trees, Schools, & Stakeholders: Building Partnerships For Schoolyard Greening With Portland Urban Forestry Learning Landscapes, Claire Brase

Environmental Science and Management Professional Master's Project Reports

Portland Urban Forestry Learning Landscapes is a city program offering free tree planting and establishment services to schools, community groups, and multi-residential zones in the city of Portland, Oregon. This project report, completed on behalf of Learning Landscapes, uses qualitative evidence from interviews with school district administrators, teachers, volunteers, grounds staff, city employees, and outreach experts to gain a deeper understanding of the factors leading to successful collaboration between local school districts and their external community partners. These conversations with school and outreach personnel across the city suggest that district staff are interested in collaborative greening organizations like Learning Landscapes …

Eviction Court In Multnomah County: Improved Legal Outcomes For Tenants Amidst Rising Eviction Filings, Alex Farrington, Lisa K. Bates, Colleen Carroll, Yi Wang, Azad Amir-Ghassemi, Minji Cho, Natalie J. Cholula, Andrew Lindstrom

Urban Studies and Planning Faculty Publications and Presentations

To examine how court processes at the Multnomah County Circuit Court impact tenants, our team conducted a series of court observations at the downtown courthouse between October and December 2023. In particular, we examined how the court handled the increasing size of eviction dockets during this period, the availability of legal assistance and other onsite services for tenants, the court’s implementation of new rules from Oregon House Bill 2001 (HB 2001), and any changes HB 2001 had on eviction case outcomes in Multnomah County. From our court observations, we found that high-volume eviction dockets were handled in a way that …

"Where Will My Family Go?" The Financial, Housing, And Health Challenges Of Families Facing Eviction, Natalie J. Cholula, Alex Farrington, Lisa K. Bates, Marisa A. Zapata, Jacen Greene

Urban Studies and Planning Faculty Publications and Presentations

To better understand how eviction affects families with children, the Evicted in Oregon team conducted focus groups with 74 tenants from Baker, Lincoln, Linn, Marion, Multnomah, and Washington counties in Oregon. During focus group discussions, we asked participants whether they had children in the household at the time of eviction and how it impacted them. Parents reported that their children’s well-being was their main concern during the eviction process. They also described how eviction led to disruptions in their children’s education, health and medical treatments, and living arrangements. Based on our findings, we provide a set of policy and program …

“I Have Nowhere To Go” : An Examination Of Post-Eviction Housing Outcomes Of Evicted Tenants, Alex Farrington, Natalie J. Cholula, Lisa K. Bates

Urban Studies and Planning Faculty Publications and Presentations

Given the extent of the eviction crisis in Oregon, it is crucial to investigate what happens to tenants after they are displaced from their homes. This report investigates three main questions concerning tenants’ post-eviction housing outcomes: Where do tenants live after they have been evicted? How do post-eviction housing outcomes affect tenants’ health, finances, and safety? How do rental assistance and legal assistance affect post-eviction housing outcomes?

Nearly two thirds of evicted tenants moved directly into some form of homelessness, including sheltered, unsheltered, or doubled-up homelessness. These findings indicate that Oregon’s eviction crisis and homelessness crisis are closely linked.

Tenants …

Rock: Riesz Occupation Kernel Methods For Learning Dynamical Systems, Victor W. Reilly, Chau Nguyen, Kamel Lahouel, Anthony Kolshorn, Nicholas Fisher, Bruno Jedynak

Mathematics and Statistics Faculty Publications and Presentations

We present a representer theorem for a large class of variational problems, generalizing regression, finite element methods, and providing techniques for learning partial and ordinary differential equations (PDEs and ODEs). We apply our formulation to the multivariate occupation kernel method (MOCK) for learning dynamical systems from data. Our generalized method outperforms the MOCK method on most tested datasets, while often requiring considerably fewer parameters. Experiments are presented for learning ODEs and PDEs.

Mock: An Algorithm For Learning Nonparametric Differential Equations Via Multivariate Occupation Kernel Functions, Victoria A. Rielly, Kamel Lahouel, Ethan Lew, Nicholas Fisher, Vicky Geneva Haney, Michael Wells, Bruno M. Jedynak

Mathematics and Statistics Faculty Publications and Presentations

Learning a nonparametric system of ordinary differential equations from trajectories in a -dimensional state space requires learning functions of variables. Explicit formulations often scale quadratically in unless additional knowledge about system properties, such as sparsity and symmetries, is available. In this work, we propose a linear approach, the multivariate occupation kernel method (MOCK), using the implicit formulation provided by vector-valued reproducing kernel Hilbert spaces. The solution for the vector field relies on multivariate occupation kernel functions associated with the trajectories and scales linearly with the dimension of the state space. We validate through experiments on a variety of simulated and …

The Matryoshka Doll Prior – Principled Multiplicity Correction In Bayesian Model Comparison, Andrew Womack, Daniel Taylor-Rodríguez, Claudio Fuentes

Mathematics and Statistics Faculty Publications and Presentations

This paper introduces a general and principled construction of model space priors with a focus on regression problems. The proposed formulation regards each model as a “local” null hypothesis whose alternatives are the set of models that nest it. Assuming constant ratio of prior probabilities for any “local” null and its alternatives provides a natural isomorphism of model spaces (like a matryoshka doll), constituting an intuitive way to correct for multiplicity in Bayesian model selection and averaging problems. This isomorphism yields the Poisson distribution as the unique limiting distribution over model dimension under mild assumptions. We compare this model space …

Belonging: Data Reasoning, About Ourselves And Others, Eva Thanheiser, Molly Robinson, Simon Byeonguk Han, Amanda Sugimoto, Courtney Koestler, Mathew Felton-Koestler

Mathematics and Statistics Faculty Publications and Presentations

Using Watson’s belonging framework and GAISE II data reasoning, this lesson invites students to collect, analyze, and represent personal data. In doing so, they see themselves and their peers reflected in the work, reinforcing their value as active participants in both mathematical thinking and the classroom community.

Public Participation Geographic Information Systems (Ppgis) And Understanding User Conflict On The Rogue River, Kim Gaines-Munkvold

Masters in GIS Practicum Reports

This practicum explored the application of Public Participation Geographic Information Systems (PPGIS) to better understand user conflict on the Upper Rogue River, particularly tensions surrounding the use of jetboats. Partnered with Oregon’s Kitchen Table (OKT), who specialize in public engagement within the Hatfield School of Governance at Portland State University, the project supported Oregon state agencies’ effort to gather community input. The practicum consisted of two components - field work and Geographic Information Systems (GIS) analysis. Field work involved traveling to southern Oregon to work as a part of OKT public engagement team at community events and conversations, using a …

Foundation Models Boost Low-Level Perceptual Similarity Metrics, Abhijay Ghildyal, Nabajeet Barman, Saman Zadtootaghaj

Computer Science Faculty Publications and Presentations

For full-reference image quality assessment (FR-IQA) using deep-learning approaches, the perceptual similarity score between a distorted image and a reference image is typically computed as a distance measure between features extracted from a pretrained CNN or more recently, a Transformer network. Often, these intermediate features require further fine-tuning or processing with additional neural network layers to align the final similarity scores with human judgments. So far, most IQA models based on foundation models have primarily relied on the final layer or the embedding for the quality score estimation. In contrast, this work explores the potential of utilizing the intermediate features …

“Lost-In-The-Later”: Framework For Quantifying Contextual Grounding In Large Language Models, Yufei Tao, Adam Hiatt, Rahul Seetharaman, Ameeta Agrawal

Computer Science Faculty Publications and Presentations

Large language models (LLMs) are capable of leveraging both contextual and parametric knowledge but how they prioritize and integrate these sources remains underexplored. We introduce CoPE, a novel framework for systematically quantifying contextual grounding in LLMs. CoPE distinguishes between contextual knowledge (CK) and parametric knowledge (PK), enabling fine-grained attribution across languages and tasks. Using our newly created MultiWikiAtomic dataset in English, Spanish, and Danish, we analyze how LLMs integrate context, prioritize information, and incorporate PK in open-ended question answering. We find that across models and languages, only around 50 to 76 percent of outputs are grounded in the given context, …

Drta: Dynamic Reward Scaling For Reinforcement Learning In Time Series Anomaly Detection, Bahareh Golchin, Banafsheh Rekabdar, Kunpeng Liu

Computer Science Faculty Publications and Presentations

Anomaly detection in time series data is important for applications in finance, healthcare, sensor networks, and industrial monitoring. Traditional methods usually struggle with limited labeled data, high false-positive rates, and difficulty generalizing to novel anomaly types. To overcome these challenges, we propose a reinforcement learning-based framework that integrates dynamic reward shaping, Variational Autoencoder (VAE), and active learning, called DRTA. Our method uses an adaptive reward mechanism that balances exploration and exploitation by dynamically scaling the effect of VAE-based reconstruction error and classification rewards. This approach enables the agent to detect anomalies effectively in low-label systems while maintaining high precision and …

Scitopic: Enhancing Topic Discovery In Scientific Literature Through Advanced Llm, Pengjiang Li, Zaitian Wang, Xinhao Zhang, Ran Zhang, Lu Jiang, Pengfei Wang, Yuanchun Zhou

Computer Science Faculty Publications and Presentations

Topic discovery in scientific literature provides valuable insights for researchers to identify emerging trends and explore new avenues for investigation, facilitating easier scientific information retrieval. Many machine learning methods, particularly deep embedding techniques, have been applied to discover research topics. However, most existing topic discovery methods rely on word embedding to capture the semantics and lack a comprehensive understanding of scientific publications, struggling with complex, high-dimensional text relationships. Inspired by the exceptional comprehension of textual information by large language models (LLMs), we propose an advanced topic discovery method enhanced by LLMs to improve scientific topic identification, namely SciTopic. Specifically, we …

Portland's Urban Doom Loop, Noah Rappaport, Avery Kageyama, Austen Knight

Business Faculty Publications and Presentations

In this analysis we define the system boundary as Portland's downtown core, encompassing the downtown and immediately adjacent districts immediately affected by the effects of the "Urban Doom Loop." We selected this boundary for the concentration of reinforcing feedback loops. In Portland's downtown core remote work, commercial vacancy, homelessness, public safety concerns, and narrative media depiction of decline converge and amplify each other most intensely. Media focus on downtown disproportionately shapes public perceptions of the entire city. Interventions by city agencies are being proposed to take place there. While acknowledging that Portland's challenges extend beyond the downtown core, we intentionally …

Grid Services Demonstration Using Service-Oriented Derms, Midrar Adham, Sean Keene, Tylor E. Slay, Jaime T. Kolln, Robert Bass

Electrical and Computer Engineering Faculty Publications and Presentations

This work demonstrates four grid service use cases using a service-oriented DER Management System within an Energy Grid of Things network. Imposed by a set of rules referred to as the Energy Service Interface, the DER Management System uses IEEE 2030.5 as its primary communication protocol. To simulate the efforts of Grid Operators, Grid Service Providers, and consumers to provide grid services, a co-simulation environment that leverages the GridAPPS-D simulation platform was developed. The results show that a service-oriented DER Management System provides a means of achieving reliable and impactful grid services while protecting customers’ privacy and ensuring their agency …

Unsupervised Oct Image Interpolation Using Deformable Registration And Generative Models, Shuwen Wei, Samuel W. Remedios, Bruno Jedynak, Peter Calabresi, Shiv Saidha, Aaron Carass, Multiple Additional Authors

Mathematics and Statistics Faculty Publications and Presentations

Optical coherence tomography (OCT) images are often acquired as highly anisotropic volumes, where the scanning step is dense along the fast axis but sparse along the slow axis. This affects image analysis, such as image registration for longitudinal alignment. To create more isotropic volumes, bicubic interpolation can be used along the slow axis, but it generally produces blurry features. Registration-based interpolation can reduce blurriness, but often fails to generate realistic OCT images. Deep generative models can sample realistic images, but lack the structural consistency constraints required for interpolation. In this paper, we propose an unsupervised image interpolation method that combines …

Assisted Living, Residential Care, And Memory Care Staffing Level Dynamics: Oregon, 2017–2023, Sarah Dys, Ozcan Tunalilar

Institute on Aging Publications

Objectives

To examine changes in staffing levels over time in Oregon assisted living and residential care (AL/RC) communities between 2017 and 2023.

Design

Longitudinal study of licensed AL/RC communities.

Setting and Participants

A total of 1720 setting-year observations from 535 individual AL/RC communities in Oregon between 2017 and 2023.

Methods

Primary data were collected through the Oregon Community-Based Care (CBC) study. Staffing levels were estimated as care staff hours per resident per day and reported overall and by staff type, including registered nurses, licensed practical nurses, certified nursing assistants, certified medication aides, unlicensed care staff, life enrichment staff, and social …

Sound, Touch, And Place: An Exploratory Study Of The Potential For Emotive Cognitive Mapping Through The Use Of Audio Description And Tactile Objects At A National Memorial, Amy Parker, Haruka Hopper, Kira Swearingen, Leah Neumann Samson, Brett Oppegaard, Hazelle Magaoay-Baniaga, Mary-Clare Cable

Leadership, Learning and Counseling Faculty Publications and Presentations

Audio description, tactile objects, and memorial sites are often studied separately for their communicative and emotive potential without consideration of potential multimodal connections existing among people, technologies, and solemn public places. This study’s novel mixed-methods approach uses site tours, surveys, think-aloud protocol, semi-structured interviews, and observations to develop rich and holistic understandings of such complex multimodal site visits at Pearl Harbor National Memorial in Hawai‘i by people who are DeafBlind, blind, or with low vision. Several themes emerged in the grounded empirical data gathered during the tour, afterward, and during an interview one month later, including findings that bridged emotions, …

Descending An Inclined Plane With A Large Language Model, Justin C. Dunlap, Ryan Sissons, Ralf Widenhorn

Physics Faculty Publications and Presentations

[This paper is part of the Focused Collection in Artificial Intelligence Tools in Physics Teaching and Physics Education Research.] We present a study in which a version of a common conservation of mechanical energy introductory physics problem, an object released on an inclined plane, is given to OpenAI’s GPT-4 large language model (LLM). We investigate how different permutations of object, action verb, and property of the incline impact the responses of the LLM. The problem setup and prompting was left purposefully minimal, requiring the LLM to state multiple assumptions to justify the final answer. We specifically studied how different keywords …

Thermal Regimes Of Beaver-Impacted Stream Reaches In The Tualatin River Basin, Oregon, United States, Miles Mayer, Heejun Chang

Geography Faculty Publications and Presentations

As beavers are increasingly used in stream restoration, interest in their potential to mitigate stream degradation has grown. However, the impacts of beaver dams on stream temperature remain inconsistent across studies, often due to differences in spatial scale and methodology. We investigated summer thermal regimes at multiple spatial scales in three beaver-impacted streams within the Tualatin River Basin, Oregon, using fiber-optic distributed temperature sensing. Substantial variability in thermal regimes was observed among the three stream reaches and around individual beaver dams within each reach. This variability was primarily influenced by geomorphic changes in stream depth associated with beaver dams and …

Whipped Into Shape: Infrastructure As Punishment For The Unruly Citizen, Eliza Mortimer

Anthós

This article interrogates how inflexible designs and policies of airline seating, as well as critical socio-political rhetoric, discipline and marginalize bodies that do not conform to normative standards. Situating airline seating within fat and disability studies models, Mortimer argues that these punitive measures are not neutral, but rather reflective of cultural narratives of productivity, morality, and bodily discipline. Mortimer draws from ethnographic analyses of first-person accounts and policy reviews to demonstrate how shrinking seats, rigid policies, and moralized discourse construct a “space of calculability” that pressures fat individuals to monitor and minimize their bodies to justify their presence in public …

A Proposed Ehrenfeucht-Fraïssé Game Model For Natural Language Processing Generative Adversarial Networks, Don Li

Anthós

Large Language Models (LLM’s) (e.g., ChatGPT) constitute both a significant research area and commercial application of AI. Current major LLM’s are built on Generative Pre-Trained Transformer (GPT) neural network architecture to perform natural language processing (NLP) tasks. Generative Adversarial Network (GAN) is another popular neural network architecture, which leverages a zero-sum game between constituent neural networks within the architecture to train the GAN, and is widely used for visual data applications. This article proposes a new GAN architecture for NLP: an EF-GAN whose underlying algorithm uses Ehrenfeucht–Fraïssé (EF) games, a game-theoretic approach from model theory to determine elementary equivalence of …

Voices To Faces: The Midnight Cowboy’S Picaresque Journey, Dave Patterson

Anthós

John Schlesinger’s 1969 film, Midnight Cowboy, tells the story of Joe Buck, a Texan who dreams of working as a hustler in New York. Schlesinger’s resulting tale follows the framework of the picaresque, wherein a naive rube ventures to the big city to pursue a better life and exposes societal ills along the way. In this article, Patterson reveals how Joe Buck’s internal tension derives from his materialism, symbolized as disembodied voices, and his devaluing of human connection and empathy, symbolized as faces. The voices from his radio’s advertisements and game shows reinforce his flawed assumptions about life. A midpoint …

Seizing The Moment: Post-Covid-19 Enrollment Trends Among Nontraditional Honors Students, Solomon Aleph

Anthós

This article investigates the motivations behind the rising enrollment of nontraditional students at Portland State University’s Honors College, which nearly doubled from 2019 to 2022 following the COVID-19 pandemic. Aleph argues that nontraditional students viewed education as a means of reclaiming agency and reshaping their futures. Through interviews and a focus group with four nontraditional students, Aleph explores how the pandemic created disruption and opportunities. The study finds that while economic instability and isolation were factors, intrinsic motivations—especially a desire for self-improvement, intellectual challenge, and rewriting personal narratives—were more prominent factors shaping enrollment decisions. Drawing from this qualitative analysis, Aleph …

Minoru’S Mecca: The World Trade Center, 9/11, And The End Of Architectural Modernism, Henry D. Senters

Anthós

From 1966 until 2001, the World Trade Center complex and its skyscraper Twin Towers were a defining architectural feature of New York City and a landmark in architectural modernism. Minoru Yamasaki, the project’s architect, intended the massive complex to be a “mecca” that drew in and inspired urban workers, exemplifying the Islamic architectural language that he incorporated into his architecture. In this article, Senters examines how architectural criticism evolved while the towers stood and after they were destroyed in the 9/11 terrorist attack. Tracking early reviews, hopeful about the complex’s trade-promoting purpose, and the perennial—though limited—negative reception of the complex’s …