Digital Commons Network^™

Governmental Suppression Of Speech On The Internet: Reflections On The Murthy Decision, Russell L. Weaver

Louisiana Law Review

No abstract provided.

Bondholders Motion To Dismiss

Catholic Dioceses in Bankruptcy

No abstract provided.

Limitations Of Using Large Language Models For Automated Essay Scoring, Thomas A. Fink

Theses

Background: Automated essay scoring (AES) is a challenging deep learning problem. The two most widely used methods for predicting essay quality scores, supervised learning-based and LLM-based, have their own limitations. Although supervised learning-based methods are more accurate, they only predict a score and do not offer descriptive feedback to students. On the other hand, LLM-based methods can offer rubric-guided feedback but are known to be less accurate.

Methods: This work focuses on improving the accuracy of state-of-the-art LLM-based AES methods. We began by thoroughly investigating why these methods were performing poorly for certain datasets and certain examples. This led us …

Ucf Libraries 2025 Faculty Research Showcase, Jasmine George, Patrick Green, Joe Bizon, Kristine Shrauger, Ying Zhang, Renee Cole Montgomery, Rachel Trnka, Mary Rubin, Min Tong, Arielle Petrovich, Jeanne Piascik, Sai Deng

Faculty Scholarship and Creative Works

The University of Central Florida (UCF) Libraries’ Faculty Research Showcase is a longstanding tradition that celebrates the scholarly achievements, research, and creative work of library faculty.

The 2025 Showcase features eight engaging presentations covering a diverse range of topics:

• Jasmine George – Student Engagement Initiatives Around Florida Academic Libraries

• Jasmine George & Patrick Green – Student Wellbeing at the Library

• Joe Bizon, Kristine Shrauger, Ying Zhang & Renee Cole Montgomery – Roadmapping Leganto Successfully

• Rachel Trnka – Florida Library History

• Mary Rubin – Escape the Ordinary: Escape Rooms as Active Learning Magic

• Min Tong …

Predicting Sleep And Sleep Stage In Children Using Actigraphy And Heartrate Via A Long Short-Term Memory Deep Learning Algorithm: A Performance Evaluation, Robert Weaver Med, Phd, James White, Olivia Finnegan, Hongpeng Yang, Zifei Zhong, Keagan Kiely, Catherine Jones, Yan Tong, Srihari Nelakuditi, Rahul Ghosal, David E. Brown, Russell R. Pate Ph.D., Gregory J. Welk, Massimiliano De Zambotti, Yuan Wang, Sarah Burkart, Elizabeth L. Adams Phd, Bridget Armstrong, Michael Beets Med, Mph, Phd

Faculty Publications

Children's ambulatory sleep is commonly measured via actigraphy. However, traditional actigraphy measured sleep (e.g., Sadeh algorithm) struggles to predict wake (i.e., specificity, values typically < 70) and cannot predict sleep stages. Long short-term memory (LSTM) is a machine learning algorithm that may address these deficiencies. This study evaluated the agreement of LSTM sleep estimates from actigraphy and heartrate (HR) data with polysomnography (PSG). Children (N = 238, 5–12 years,52.8% male, 50% Black 31.9% White) participated in an overnight laboratory polysomnography. Participants were referred be-cause of suspected sleep disruptions. Children wore an ActiGraph GT9X accelerometer and two of three consumer wearables(i.e., Apple Watch Series 7, Fitbit Sense, Garmin Vivoactive 4) on their non-dominant wrist during the polysomnogram. LSTM estimated sleep versus wake and sleep stage (wake, not-REM, REM) using raw actigraphy and HR data for each 30-s epoch. Logistic regression and random forest were also estimated as a benchmark for performance with which to compare the LSTM results. A 10-fold cross-validation technique was employed, and confusion matrices were constructed. Sensitivity and specificity were calculated to assess the agreement between research-grade and consumer wearables with the criterion polysomnography. For sleep versus wake classification, LSTM outperformed logistic regression and random forest with accuracy ranging from 94.1to 95.1, sensitivity ranging from 94.9 to 95.9 across different devices, and specificity ranging from 84.5 to 89.6. The addition of HR improved the prediction of sleep stages but not binary sleep versus wake. LSTM is promising for predicting sleep and sleep staging from actigraphy data, and HR may improve sleep stage prediction.

Middle Schoolers Explore The Rule Of Law At Second Annual Mandell-Boisclair Justice Camp 7-17-2025, Jordan J. Phelan

School of Law Conferences, Lectures & Events

No abstract provided.

Law School News: Middle Schoolers Explore The Rule Of Law At Second Annual Mandell-Boisclair Justice Camp 07-17-2025, Jordan J. Phelan

Life of the Law School (1993- )

No abstract provided.

Enjoined From Enjoining: The State Of Remedies And Relief For Immigrants After Garland V. Aleman Gonzalez, Kate Weaver

Louisiana Law Review

No abstract provided.

“Newcomer” Injunctions—Quia Timet Plus, David Capper

Louisiana Law Review

No abstract provided.

Remedies In The Social Media Context: “Calling Out” Design Appropriation, Nyssa Bull, Robyn Carroll, Janie Mccutcheon, Fiona Mcgaughey

Louisiana Law Review

No abstract provided.

Preventing The Police From Using Private Information Obtained Through An Unlawful Search – An Australian Perspective, Sirko Harder

Louisiana Law Review

No abstract provided.

A Tale Of Two Disclosure Requirements, Leslie Gielow Jacobs

Louisiana Law Review

No abstract provided.

Class Action Cy Près Revisited, Christopher J. Ryan Jr.

Louisiana Law Review

No abstract provided.

Algorithmic Erasure In The Shadow Of Statutes, Caprice L. Roberts

Louisiana Law Review

No abstract provided.

Class Actions And The Restitutionary Disgorgement Remedy For Torts: The Unfortunate Triumph Of The Parasitic Theory, John D. Mccamus

Louisiana Law Review

No abstract provided.

Give Scotus’S Code Of Conduct Teeth: Using Impeachment To Enforce The United States Supreme Court’S Code Of Conduct, Tara Roussel

Louisiana Law Review

No abstract provided.

Can Commentary Command Courtroom Clout?: The Court’S Deference To Commentary To The United States Sentencing Guidelines In A Post-Kisor World, Gabriella A. Leccese

Louisiana Law Review

No abstract provided.

Beyond The Surface: How Depth Alters Face Perception, Farid Pazhoohi, Jerry Wang, Keina Aoki, Alan Kingstone

School of Psychology

Research in face perception has predominantly utilized two-dimensional images, which does not fully capture the complexities of human perception as it operates in real-world settings. Previous studies have demonstrated that 3D objects elicit different neural and behavioral responses compared to their 2D counterparts, suggesting a more profound engagement with and processing of real-world objects and environments. Grounded in the understanding that human visual perception has evolved in three-dimensional environments, this research addressed a notable gap in the literature on facial perception. This study investigates the impact of presentation modality (2D vs. 3D) on the perception of facial attractiveness, dominance, and …

The Local Neighborhood Interactions Shaping Tree Communities Through Ecological Disturbance And Environmental Change, Cole Doolittle

Dissertations (1934 -)

My dissertation consists of four chapters focused on understanding the factors maintaining local species diversity, addressing a central goal of ecology increasingly critical for preserving ecosystem services and human well-being in the face of climate change. Specifically, I investigate how local neighborhoods – that is, the spatial arrangement, density, and identity of nearby competitors – contribute to patterns of growth and survival along gradients of ecological disturbance and environmental change using forest tree communities as a study system. In my first chapter, I develop a conceptual framework that synthesizes why we may expect local neighborhood interactions to weaken under ecological …

Annual Ghg Emissions In Co2e From Turfgrass Maintenance In Los Angeles County, Valerie Rountree, Michael Barnes, Martin S. Hoecker-Martinez, Nathan W. Strout

Cities and the Environment (CATE)

Maintaining turfgrass landscapes - including mowing, irrigation, and fertilization - is an under-recognized source of greenhouse gas (GHG) emissions, particularly carbon dioxide and nitrous oxides. The state of California has ambitious policies to reduce GHG emissions and improve local air quality, however, southern California continues to be one of the most polluted regions in the world; moreover, it is an arid region that relies heavily on imported water to meet diverse user needs. To better understand the significance of turfgrass maintenance as a source of GHG emissions, we calculate the total annual GHG emissions in carbon dioxide equivalent (CO_{2 …}

Ethical Lawyering While Our House Is On Fire, Jaclyn Lopez

Maryland Journal of International Law

No abstract provided.

A Study On Litigation Strategies Of Environmental Ngos In China’S Centralized Governance Society: Taking Biodiversity Litigation As An Example, Huiyu Zhao, Yizhen Xu

Maryland Journal of International Law

No abstract provided.

Strategic To Whom? Uncovering The Hidden Costs Of Lithium Extraction To Zimbabwean Communities, Obert Bore, Manele Mpofu

Maryland Journal of International Law

No abstract provided.

Reflections On The Clinical Teaching Model: The Experience Of The Alliance Of Environmental Law Clinics Of Latin America And The Caribbean, Andrea Dominguez, Andrés Dulanto

Maryland Journal of International Law

No abstract provided.

A Human Rights-Based Litigation Strategy For Wildlife Protection In Nigeria, Emmanuel K. Omirin

Maryland Journal of International Law

No abstract provided.

Suppression For The Sake Of Democracy: Brazil’S Judiciary And The Fight To Control The Internet, Isaiah Hinton

Maryland Journal of International Law

No abstract provided.

Native Hawaiians And Environmental Stewardship: Reclaiming The ‘Āina In The Face Of A Western System, Lauren Wiederkehr

Maryland Journal of International Law

No abstract provided.

Diffusion Model For Medical Image Denoising, Reconstruction And Translation, Wei Wang, Shuo Li

Faculty Scholarship

Diffusion models, as a class of generative models, have demonstrated significant performance in image generation since their inception. The fundamental principle behind diffusion models is the definition of a forward process and a reverse process. The input data is progressively perturbed by adding random noise during the forward process, and the expected noise distribution is learned. In the reverse process, noise is gradually reduced from a Gaussian distribution to generate the image. Recently, diffusion models have been widely adopted in various image processing tasks, including text-to-image synthesis, denoising, segmentation, and object detection. In medical image analysis, diffusion models have shown …

Utility Of The Us Preventive Services Task Force For Preeclampsia Risk Assessment And Aspirin Prophylaxis, Thomas Mcelrath, Arun Jeyabalan, Arkady Khodursky, Alison Moe, Manfred Lee, Maneesh Jain, Laura Goetzl, Elizabeth Sutton, Pamela Simmons, George Saade, Antonio Saad, Luis Pacheco, Esther Park-Hwang, Antonina Frolova, Ebony Carter, Ai-Ris Collier, Daniel Kiefer, Vincenzo Berghella, Rupsa Boelig, Michal Elovitz, Cynthia Gyamfi-Bannerman, Joseph Biggio, Kara Rood, William Grobman, Carrie Haverty, Morten Rasmussen

Department of Obstetrics and Gynecology Faculty Papers

IMPORTANCE: The US Preventive Services Task Force (USPSTF) guidelines on preeclampsia risk assessment and aspirin prophylaxis (AP) have not been evaluated for clinical utility.

OBJECTIVE: To evaluate which characteristics in the USPSTF guidelines identify risk status and the association of preeclampsia risk with AP recommendations.

DESIGN, SETTING, AND PARTICIPANTS: This observational cohort study enrolled from July 2020 to March 2023 with data analysis performed from October to December 2024. Enrollment occurred at 11 centers throughout the US or via direct-to-participant recruitment. Pregnant participants aged 18 years or older with a singleton pregnancy less than 22 weeks' gestation were selected via …

Shutdowns And Startups: How Covid-19 Policies Shaped Entrepreneurial Trends Across U.S. States, Mia Sideris

Business and Economics Summer Fellows

The COVID-19 pandemic had an unprecedented impact on the US economy. Severe restrictions were placed on nearly every state, culminating with the declaration of a national emergency in March 2020. The intent of these restrictions was to safeguard public health, while unintentionally imposing operational burdens on businesses. This paper explores how heterogeneity in state level policy responses influenced entrepreneurial activity. Using the synthetic control method and data on mandated workplace closures, we estimate the impact of these actions on business applications per capita in the state of South Carolina, which was late to impose these measures. We find that if …