Digital Commons Network^™

Assessment Of Spatial Autocorrelation And Scalability In Fine-Scale Wildfire Random Forest Prediction Models, Madeleine Pascolini-Campbell, Joshua B. Fisher, Kerry Cawse-Nicholson, Christine M. Lee, Natasha Stavros

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Wildfire prediction models that can be applied across diverse regions at fine scales (<  100 m) are critical for wildfire management. Remote sensing offers a path forward by providing heterogeneous and dynamic measurements of fuel load, type, and flammability. Machine learning methods such as random forests provide an empirical framework that are high-accuracy, computationally efficient, interpretable and able to model complex ecological relationships. Here we use high resolution (70 m, every 3–5 days) remote sensing observations of evapotranspiration and evaporative stress index, which represent plant water stress, from Ecosystem Spaceborne Thermal Radiometer on Space Station (ECOSTRESS), as well as topography and weather data, to predict burn severity and occurrence for 8 large wildfires that burned 3715 km² from 2021 and 2022 in New Mexico, USA. These fires ranged from low to high burn intensity, and covered a diverse range of ecoregions (deserts, grasslands, forests), plant species, and topographies. We used a single model to predict the burn severity of all wildfires one week before occurrence. The prediction accuracy was greatest when using all predictors (ECOSTRESS, weather, topography) (R² = 0.77). We assessed the role of spatial autocorrelation in driving model performance by: (1) increasing the sample spacing of our dataset, (2) …

Interview With Jim Woodhead, Jacey Terrill

What Happened Here

This is a transcript of an oral history with Jim Woodhead, who is part of the staff of the Art Department at Cal Poly Humboldt as a studio art technician. He was responsible for making sure students who took art classes were able to retrieve their belongings, including expensive art supplies and artworks, when the campus was closed. He spent multiple weeks coordinating with students, making sure those who needed their items as soon as possible were prioritized. He is highly critical of some of the protesters’ choice to vandalize, noting the amount of work staff had to put in …

Full-Stack Web Applications: Infrastructure, Development Pipelines & Devsecops, Yassine Chahid, Patrick Slattery

Publications and Research

This research explores emerging development methodologies and technologies which facilitate the deployment and maintenance of software applications. It evaluates architectural styles for the development of software such as monolithic (legacy) and microservice models, with a focus on their key differences such as scalability or project structure through to the development of an application. By examining methodologies such as Agile and continuous integration/continuous development pipelines along with the deployment tools Docker and Git for version/release control, the study analyzes how these innovations speed up development, improve existing practices, and serve as the foundation for development operations. Cloud solutions for tasks such …

Cross-Cultural Data On Romantic Love And Mate Preferences From 117,293 Participants Across 175 Countries, Marta Kowal, Piotr Sorokowski, Biljana Gjoneska, Katarzyna Pisanski, Gerit Pfuhl, Leonardo Aguilar, Steve M.J. Janssen, Benjamin Gelbart, Patrícia Arriaga, Jan Antfolk, Katarina Zvončáková, Linda H. Lidborg, Jorge Contreras-Garduño, Mikhail V. Kozlov, Taciano L. Milfont, Marco A.C. Varella, Valerija Križanić, Mahmoud Boussena, Tina Kavčič, Diana Ribeiro Da Silva, Brahim Hamdaoui, Fatima Zahra Sahli, Karlijn Massar, Eliane Deschrijver, Tatsunori Ishii, Hakan Cetinkaya, Oksana Senyk, Farida Guemaz, Koen Ponnet, Yahya Don, Dušana Šakan, Gyesook Yoo, Ravit Nussinson, Joaquín Ungaretti, Ali R. Can, Izzet Duyar, Jiří Čeněk, Joao Carneiro, Norbert Meskó, Luca Kozma, Ellen K. Nyhus, Mona Vintila, Oulmann Zerhouni, Farid Pazhoohi, Maja Zupančič, Sinem Söylemez, Austin H.-E. Wang, Marietta Papadatou-Pastou, Irena Pavela Banai, Pavol Prokop, Mohd Sofian Omar Fauzee, Reza Afhami, Jean C. Natividade, Roberto Baiocco, Mara Morelli, Toivo Aavik, Ezgi Toplu-Demirtaş, Singha Tulyakul, Anna Wlodarczyk, Razleh Chegeni, Anabela C. Santos, Dmitry Grigoryev, Dmitrii Dubrov, Dimitri Chubinidze, Gözde Ikizer, Nana Burduli, Johanna Czamanski-Cohen, Rizwana Amin, Petros Roussos, Evgeniya Hristova, Rūta Sargautytė, Ekaterine Pirtskhalava, Tenuunjargal Avirmed, Arooj Najmussaqib, Abdelilah Charyate, Shagufta Batool, Tatiana Volkodav, Yoshihiko Kunisato, Yuki Yamada, Asako Toyama, Mariia Perun, Seda Dural, Tetyana Mandzyk, Anna Studzinska, Ognen Spasovski, Felipe E. García, Caterina Grano, Merve Boğa, Mehmet Koyuncu, Sangeeta Singh, Ju Hee Park, Derya Atamtürk, Samuel Lins, Martin Pírko, David Lacko, Balazs Aczel, Ferenc Kocsor, Ádám Putz, Tobias Otterbring, Pavol Kačmár, Efisio Manunta, Théo Besson, Nasim Ghahraman Moharrampour, Çağlar Solak, Ignacio Estevan, Merve Topcu Bulut, Nicolas Kervyn, Moises Mebarak, Jackson G. Lu, Nejc Plohl, Bojan Musil, Adil Samekin, Kirill G. Miroshnik, Clément Cornec, Isabella Giammusso, Ulf-Dietrich Reips, Maria Rosa Miccoli, Miriam Parise, Sabrina Stöckli, Tiago Marot, Sibele D. Aquino, Amanda Londero-Santos, Antonio Chirumbolo, Aybegum Memisoglu-Sanli, Jaroslava V. Valentova, Cemre Karaarslan, Ivana Hromatko, Kevin S. Kertechian, Ogeday Çoker, Matheus F. F. Ribeiro, Carlota Batres, Ilker Dalgar, Stephanie J. Eder, Katarina Mišetić, Marios Argyrides, Vita Mikuličiūtė, Silvia Mari, Elisabeth Oberzaucher, Kathrin Masuch, Alan D. A. Mattiassi, Salma Samir Omar, Elena Piccinelli, Eda Ermagan Caglar, Diogo Lamela, David A. Frederick, Aleksander Kobylarek, Ma. Criselda T. Pacquing, Marc Eric Santos Reyes, Marcos Zumárraga-Espinosa, Feten Fekih-Romdhane, Talía Gómez Yepes, Edgardo Etchezahar, Katarzyna Galasinska, Jan P. Röer, Ayşegül Şahin, Miguel Landa-Blanco, Arkadiusz Urbanek, Chee-Seng Tan, Rita Castro, Ksenija Cunichina, Anna Krasnodębska, Daniel Conroy-Beam, Franciszek Ostaszewski, Izabela Chałatkiewicz, Beatriz Abad-Villaverde, Bastien Trémolière, Alexios Arvanitis, Gulmira Topanova, Wiliam J. Chopik, Grace Akello, Ariela F. Pagani, Silvia Donato, Peter Fedor, Tomasz Frackowiak, Simon Ozer, Marlon Mayorga-Lascano, Farah Khan, Maryanne L. Fisher, Princess Lovella G. Maturan, Tatiana Semenovskikh, Sanjana Dutt, William Tamayo-Agudelo, Gulnara Ismukhanova, Laith Al-Shawaf, Luisa Angelucci, Adam Bode, Sercan Balım, Jovi C. Dacanay, Chiemezie S. Atama, Kai A. D. Morgan Campbell, Tchilissila A. Simões, Barış Özener, Paula Błauciak, Filipe Prazeres

Psychology Faculty Articles and Research

Psychological studies on close relationships have often overlooked cultural diversity, dynamic processes, and potentially universal principles that shape intimate partnerships. To address the limited generalizability of previous research and advance our understanding of romantic love experiences, mate preferences, and physical attractiveness, we conducted a large-scale cross-cultural survey study on these topics. A total of 404 researchers collected data in 45 languages from April to August 2021, involving 117,293 participants from 175 countries. Aside from standard demographic questions, the survey included valuable information on variables relevant to romantic relationships: intimate, passionate, and committed love within romantic relationships, physical-attractiveness enhancing behaviors, gender …

Student Life, Saint Mary's College Of California

Academic Catalog

No abstract provided.

Undergraduate Curriculum, Saint Mary's College Of California

Academic Catalog

No abstract provided.

Majors, Minors, And Certificates, Saint Mary's College Of California

Academic Catalog

No abstract provided.

Undergraduate Academic Regulations, Saint Mary's College Of California

Academic Catalog

No abstract provided.

Academic Officers And Services, Saint Mary's College Of California

Academic Catalog

No abstract provided.

2025-2026 Academic Catalog, Saint Mary's College Of California

Academic Catalog

No abstract provided.

Course Descriptions, Saint Mary's College Of California

Academic Catalog

No abstract provided.

The Discontinuous Petrov–Galerkin Method, Leszek Demkowicz, Jay Gopalakrishnan

Mathematics and Statistics Faculty Publications and Presentations

The discontinuous Petrov–Galerkin (DPG) method is a Petrov–Galerkin finite element method with test functions designed for obtaining stability. These test functions are computable locally, element by element, and are motivated by optimal test functions which attain the supremum in an inf-sup condition. A profound consequence of the use of nearly optimal test functions is that the DPG method can inherit the stability of the (undiscretized) variational formulation, be it coercive or not. This paper combines a presentation of the fundamentals of the DPG ideas with a review of the ongoing research on theory and applications of the DPG methodology. The …

Research On The Capacity Development Of National Science And Technology Intelligence System, Hui Li, Shu Chen, Xiao Tan

Journal of Scientific Information Research

[Purpose/significance] The unique form and systematic characteristics of great power competition, such as system-establishment and whole of society confrontation, multi-domain and multi-depth high-tech containment and counter-containment, and multi-means and multi-element mixed deterrence, determine that national scientific and technological intelligence must have considerable systemic capabilities to match and follow up.

[Method/process] This article focuses on scientific and technological intelligence institutions within the traditional system, defines the connotation of national scientific and technological intelligence system capabilities, constructs a six-dimensional capability cognition model of ""institutional improvement capability-system coordination capability-resource guarantee capability-technological innovation capability-intelligence shaping capability-safe operation capability"", and expounds its main contents.

[Result/conclusion] …

Thematic Hotspots And Strategy Analysis Of International Ai Regulatory Texts Based On Lda Models, Taitian Mao, Yihe Peng

Journal of Scientific Information Research

[Purpose/significance]This article conducts an in-depth exploration of international artificial intelligence (AI) regulatory policies and gains insights into the regulatory focuses and trends of various countries, with the aim of providing valuable references for global AI governance strategies.

[Method/process] This paper applies the LDA topic clustering analysis method to conduct an in-depth study of twenty-seven international policy documents. The aim is to accurately identify the topics, analyze the key theme words, and further reveal the regulatory hotspots in the field of artificial intelligence.

[Results/conclusion] The study reveals six core regulatory themes: systemic risk assessment, ethical and legal regulation, social impact governance, …

Culture Bears The Way, While Technology Facilitates Action: Research Of The Adoption Willingness Of Aigc In Academic Writing Among Young Scholars, Jiangfeng Liu, Zihui Wang, Zhiwei Hu, Lei Pei

Journal of Scientific Information Research

[Purpose/significance] Clarifying the influencing factors of young scholars' willingness to use AIGC and its path of action,then making effective enhancement strategies,would help to further expand the application of AIGC in academic writing.

[Method/process] This paper combs through the studies on AIGC information behavior, and identifies TOE(Technology-Organization-Envifonment，TOE) theory and research life cycle theory as the base theory. Semi-structured interviews were conducted for procedural grounded theory coding. Then, a questionnaire survey and qualitative comparative analysis using fuzzy sets were conducted to obtain the influencing factors grouping path.

[Result/conclusion] Five factors, induding content quality, system quality, perceived risk, expectation confirmation, and group norms, …

Toward A Simplified Framework For Sequential Character Recognition, Nicholas Howe

Computer Science: Faculty Publications

This paper proposes a novel approach to handwritten charac- ter recognition using convolutional non-recurrent deep neural networks. Such a network can run in parallel at every point of a document, offer- ing potential advantages in speed over recurrent approaches. The net- work’s output feeds into a beam search optimization for final decoding. Preliminary quantitative results show that the framework can achieve bootstrap training from labeled word images. It provides an alternative to sequential models that rely on connectionist temporal classification for alignment.

Analysis Of Vision Transformers And Domain Adaptation In Long-Range Facial Recognition, Zachary Michael Swanson

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Atmospheric turbulence presents a significant barrier to long-range facial recognition, introducing severe geometric distortions and blur that degrade image quality. This thesis investigates deep learning approaches for mitigating these effects, with a focus on transformer based architectures and domain adaptation strategies.

An in-depth benchmarking study was performed using convolutional neural networks (CNNs) and vision transformers (ViTs) on the Husker BRIAR Research Collection from up to 500m (HBRC-500) face dataset. The results demonstrated that vision transformers, particularly hierarchical vision transformers like the shifted-window (Swin) transformer, outperform CNN-based models at long distances due to their ability to model global spatial relationships and …

Cazyme Gene Cluster Diversity In Human Gut Microbiome, Yi Xing

Department of Food Science and Technology: Dissertations, Theses, and Student Research

In gut microbiome research, carbohydrate-active enzyme gene clusters (CGCs) have emerged as key functional units for understanding microbial glycan degradation. Unlike taxonomic or broad pathway annotations, CGCs offer gene-cluster-level resolution and capture substrate-specific microbial functions. However, their diversity and distribution in relation to host metabolic phenotypes, such as obesity, remain poorly characterized. This study tests the hypothesis that the composition and abundance of fiber-targeting CGCs vary between obese and healthy human gut microbiomes, reflecting distinct microbial carbohydrate utilization strategies. To examine this, we constructed a high-quality reference CGC dataset comprising 94,019 clusters from the Unified Human Gastrointestinal Genome and profiled …

Unm Pathology, Pathfinder Summer 2025, Unm Department Of Pathology

Newsletters

No abstract provided.

Artificial Intelligence And Humanoid Robotics: Bioethical Implications Of Replacing Human Agency In Healthcare And Beyond, Sara Feizyab, Gabriele Bonetti, Maria Chiara Medori, Cecilia Micheletti, Immacolata De Luca, Kevin Donato, Jan Miertuš, Mehmet Sait Dundar, Miroslava Vráblová, Gary Henehan, Richard Brown, Robert Marks, Stanislav Miertus, Lorenzo Lorusso, Gianluca Martino Tartaglia, Munis Dundar, Sandro Michelini, Stephen Thaddeus Connelly, Ariola Bacu, Tommaso Beccari, Ornela Gordani, Xhilda Dhamo, Eglantina Kalluci, Dominika Vešelényiová, Iveta Dirgová Luptáková, Jiri Pospíchal, Matteo Bertelli

Research Outputs: 2025-Present

The integration of advanced artificial intelligence (AI) and humanoid robotics into healthcare represents a critical evolution in biotechnology with profound societal implications. This review explores the bioethical implications of these technologies, and their potential to displace human agency in life-critical decisions. It adopts an interdisciplinary approach encompassing ethics, law, and technology. The review examines how innovations in AI and robotics might shift autonomy from humans to machines, and addresses the accountability challenges inherent in such transitions. We synthesize discussions on the ethical management of AI and robotics, underscoring the importance of maintaining human oversight and integrating ethical standards in technology …

An Exponential Cone Integer Programming And Piece-Wise Linear Approximation Approach For 0-1 Fractional Programming, Hoang Giang Pham, Thuy Anh Ta, Tien Mai

Research Collection School Of Computing and Information Systems

We study a class of binary fractional programs commonly encountered in important application domains such as assortment optimization and facility location. These problems are known to be NP-hard to approximate within any constant factor, and existing solution approaches typically rely on mixed-integer linear programming or second-order cone programming reformulations. These methods often utilize linearization techniques (e.g., big-M or McCormick inequalities), which can result in weak continuous relaxations. In this work, we propose a novel approach based on an exponential cone reformulation combined with piecewise linear approximation. This allows the problem to be solved efficiently using standard cutting-plane or branch-and-cut procedures. …

Crow: Eliminating Backdoors From Large Language Models Via Internal Consistency Regularization, Nay Myat Min, Long H. Pham, Yige Li, Jun Sun

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) are vulnerable to backdoor attacks that manipulate outputs via hidden triggers. Existing defense methods—designed for vision/text classification tasks—fail for text generation. We propose Internal Consistency Regularization (CROW), a defense leveraging the observation that backdoored models exhibit unstable layer-wise hidden representations when triggered, while clean models show smooth transitions. CROW enforces consistency across layers via adversarial perturbations and regularization during finetuning, neutralizing backdoors without requiring clean reference models or trigger knowledge—only a small clean dataset. Experiments across Llama-2 (7B, 13B), CodeLlama (7B, 13B), and Mistral-7B demonstrate CROW’s effectiveness: it achieves significant reductions in attack success rates across …

Position: Trustworthy Ai Agents Require The Integration Of Large Language Models And Formal Methods, Yedi Zhang, Yufan Cai, Xinyue Zuo, Xiaokun Luan, Kailong Wang, Zhe Hou, Yifan Zhang, Zhiyuan Wei, Meng Sun, Jun Sun, Jing Sun, Jin Song Dong

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have emerged as a transformative AI paradigm, profoundly influencing broad aspects of daily life. Despite their remarkable performance, LLMs exhibit a fundamental limitation: hallucination—the tendency to produce misleading outputs that appear plausible. This inherent unreliability poses significant risks, particularly in high-stakes domains where trustworthiness is essential. On the other hand, Formal Methods (FMs), which share foundations with symbolic AI, provide mathematically rigorous techniques for modeling, specifying, reasoning, and verifying the correctness of systems. These methods have been widely employed in mission-critical domains such as aerospace, defense, and cybersecurity. However, the broader adoption of FMs remains constrained …

Llmscan: Causal Scan For Llm Misbehavior Detection, Mengdi Zhang, Kai Kiat Goh, Peixin Zhang, Jun Sun, Lin Xin Rose, Hongyu Zhang

Research Collection School Of Computing and Information Systems

Despite the success of Large Language Models (LLMs) across various fields, their potential to generate untruthful and harmful responses poses significant risks, particularly in critical applications. This highlights the urgent need for systematic methods to detect and prevent such misbehavior. While existing approaches target specific issues such as harmful responses, this work introduces LLMSCAN, an innovative LLM monitoring technique based on causality analysis, offering a comprehensive solution. LLMSCAN systematically monitors the inner workings of an LLM through the lens of causal inference, operating on the premise that the LLM’s ‘brain’ behaves differently when generating harmful or untruthful responses. By analyzing …

Hps: Hard Preference Sampling For Human Preference Alignment, Xiandong Zou, Wanyu Lin, Yuchen Li, Pan Zhou

Research Collection School Of Computing and Information Systems

Aligning Large Language Model (LLM) responses with human preferences is vital for building safe and controllable AI systems. While preference optimization methods based on PlackettLuce (PL) and Bradley-Terry (BT) models have shown promise, they face challenges such as poor handling of harmful content, inefficient use of dispreferred responses, and, specifically for PL, high computational costs. To address these issues, we propose Hard Preference Sampling (HPS), a novel framework for robust and efficient human preference alignment. HPS introduces a training loss that prioritizes the most preferred response while rejecting all dispreferred and harmful ones. It emphasizes “hard” dispreferred responses — those …

Repairing Adversarial Texts Through Perturbation, Guoliang Dong, Jingyi Wang, Jun Sun, Sudipta Chattopadhyay, Xinyu Wang, Ting Dai, Jie Shi, Jin Song Dong

Research Collection School Of Computing and Information Systems

It is known that neural networks are subject to attacks through adversarial perturbations. Worse yet, such attacks are impossible to eliminate, i.e., the adversarial perturbation is still possible after applying mitigation methods such as adversarial training. Multiple approaches have been developed to detect and reject such adversarial inputs. Rejecting suspicious inputs however may not be always feasible or ideal. First, normal inputs may be rejected due to false alarms generated by the detection algorithm. Second, denial-of-service attacks may be conducted by feeding such systems with adversarial inputs. To address this, in this work, we focus on the text domain and …

Unbounded Multi-Hop Proxy Re-Encryption With Hra Security: An Lwe-Based Optimization, Xiaohan Wan, Yang Wang, Haiyang Xue, Mingqiang Wang

Research Collection School Of Computing and Information Systems

Proxy re-encryption (PRE) schemes enable a semi-honest proxy to transform a ciphertext of one user i to another user j while preserving the privacy of the underlying message. Multi-hop PRE schemes allow a legal ciphertext to undergo multiple transformations, but for lattice-based multi-hop PREs, the number of transformations is typically bounded due to the increase of error terms. Recently, Zhao et al. (ESORICS 2024) introduced a lattice-based unbounded multi-hop (homomorphic) PRE scheme that supports an unbounded number of hops. Nevertheless, their scheme only achieves the selective CPA security. In contrast, Fuchsbauer et al. (PKC 2019) proposed a generic framework for …

Sigfinger: A Subtle And Interactive Gnn Fingerprinting Scheme Via Spatial Structure Inference Perturbation, Ju Jia, Renjie Li, Cong Wu, Siqi Ma, Lina Wang, Robert H. Deng

Research Collection School Of Computing and Information Systems

There have been significant improvements in intellectual property (IP) protection for deep learning models trained on euclidean data. However, the complex and irregular graph-structured data in non-euclidean space poses a huge challenge to the IP protection of graph neural networks (GNNs). To address this issue, we propose a subtle and interactive GNN fingerprinting scheme through spatial structure inference perturbation, which captures the stable coordination patterns of fingerprint to guarantee the reliability of copyright verification. Specifically, the data augmentation based on adaptive graph diffusion is first exploited to generate more samples, which enables the exploration of fingerprint information from coarse to …

Instruct2see: Learning To Remove Any Obstructions Across Distributions, Junhang Li, Yu Guo, Chuhua Xian, Shengfeng He

Research Collection School Of Computing and Information Systems

Images are often obstructed by various obstacles due to capture limitations, hindering the observation of objects of interest. Most existing methods address occlusions from specific elements like fences or raindrops, but are constrained by the wide range of real-world obstructions, making comprehensive data collection impractical. To overcome these challenges, we propose Instruct2See, a novel zero-shot framework capable of handling both seen and unseen obstacles. The core idea of our approach is to unify obstruction removal by treating it as a soft-hard mask restoration problem, where any obstruction can be represented using multi-modal prompts, such as visual semantics and textual instructions, …

Action Dubber: Timing Audible Actions Via Inflectional Flow, Wenlong Wan, Weiying Zheng, Tianyi Xiang, Guiqing Li, Shengfeng He

Research Collection School Of Computing and Information Systems

We introduce the task of Audible Action Temporal Localization, which aims to identify the spatiotemporal coordinates of audible movements. Unlike conventional tasks such as action recognition and temporal action localization, which broadly analyze video content, our task focuses on the distinct kinematic dynamics of audible actions. It is based on the premise that key actions are driven by inflectional movements; for example, collisions that produce sound often involve abrupt changes in motion. To capture this, we propose T A2Net, a novel architecture that estimates inflectional flow using the second derivative of motion to determine collision timings without relying on audio …