Digital Commons Network^™

A Dose Of Accountability: Fixing Treaty Enforcement To Combat State Complicity In Transnational Illicit Fentanyl Trafficking, Caitlin Sharma

Vanderbilt Journal of Transnational Law

The international fentanyl crisis underscores significant gaps in treaty enforcement, particularly with state accountability for drug manufacturing and export practices leading to illicit fentanyl trafficking. Despite preventive measures established by United Nations drug conventions, legal loopholes in these provisions impede meaningful cooperation between state parties. This Note uses key countries, including the United States, Canada, Mexico, China, and India, as case studies in both producing and receiving fentanyl and its precursors. It evaluates current proposals to mitigate the crisis, such as enhanced international cooperation and the potential expansion of the International Criminal Court’s jurisdiction. However, these solutions remain insufficient due …

Uncharted Waters: Barriers To Ocean Carbon Removal In The High Seas Under Unclos And The Bbnj, Connor Stevens

Vanderbilt Journal of Transnational Law

No abstract provided.

Front Matter - Table Of Contents - Preface

Iowa Journal of Communication

No abstract provided.

"That's Just Not Who I Am": Rejection Of The Good Mother Ideology And Other Tools Used By Homeschool Moms To Navigate Motherhood, Gina S. Reynolds

Iowa Journal of Communication

Studying mothers often focuses on the mother-child dynamic rather than the mother as a person. Mother types, such as working mothers, often are singled out for study. One mother that is rarely considered is the homeschool mother. In homeschooling, the mother typically takes the active role in educating the children, and she receives little attention as a person (Lois, 2016). This study examines how these mother/teachers navigate their motherhood. Nine online discussions with 26 homeschooling mothers revealed strong connections to self-efficacy and agency. Agency and self-efficacy contributed to the mother’s ability to navigate and manage the tension between her needs …

Amplifying African Immigrant Women's Voices: Self-Advocacy And Competency In Maternal Healthcare Communication In The United States, Sahar Khamis, Faith Otchere, Delight Agboada

Iowa Journal of Communication

This qualitative study examines African immigrant women’s access to maternal health information (MHI), resources, and services in the United States (U.S.), the barriers and challenges they encounter when accessing and utilizing these resources, and strategies for overcoming these challenges. The study explored how African immigrant women access and share MHI, the degree of agency they exhibit in accessing maternal care, and how their participation in the study’s focus group discussions (FGDs) shaped their perceived competence and advocacy regarding maternal healthcare in the United States. Fourteen African women currently living in the United States participated in FGDs. Through these FGDs, the …

Understanding Dementia Challenges In Rural Ghana: A Systematic Review, Diana Maame Agyeiwaa Agyei, Sesi Sedegah, Abena Abokoma Asemanyi, Theresa Ayertey, Wendy Yeboah

Iowa Journal of Communication

Dementia poses an escalating public health challenge in Ghana due to an expanding aging population, yet rural awareness remains critically low. Guided by Mohan Dutta’s Culture-Centered Approach (CCA), this PRISMA-compliant qualitative systematic review synthesized ten peer-reviewed articles to examine cultural perceptions and lived experiences surrounding dementia in rural Ghana. The findings highlight that cognitive decline is widely misattributed to witchcraft or normalized as standard aging. Witchcraft misconceptions subject individuals—especially older women—to severe stigmatization, social exclusion, physical abuse, and banishment to "witch camps". Meanwhile, normalizing symptoms as ordinary aging delays critical medical care and shifts family decision-making away from patients. Furthermore, …

Chromnet: A Multi-Task Learning Framework For Cross-Cell Type Prediction Of 3d Chromatin Interactions Using Epigenetic Signals, Bin Wang, Shaokai Wang, Liqing Ding, Hongdong Li, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

The 3D organization of chromatin plays a fundamental role in gene regulation, cellular function, and disease mechanisms. However, current experimental techniques, such as Hi-C, remain costly and labor-intensive, limiting their application in large-scale and disease-related studies. To address this challenge, ChromNet is presented, a multi-task learning framework that integrates epigenetic signals across diverse cell types to enable high-precision prediction of chromatin architecture. By incorporating noise perturbation and auxiliary classification tasks, ChromNet improves the identification of topologically associating domains (TADs) and cell-type-specific chromatin structures, demonstrating superior generalization performance. Notably, ChromNet accurately predicts chromatin interactions in acute myeloid leukemia (AML) samples by …

A Comprehensive Overview Of Neurophysiological Correlates Of Cognitive Impairment In Amyotrophic Lateral Sclerosis, Seyyed Bahram Borgheai, Brie E. Achorn, Alyssa H. Zisk, Sarah M. Hosni, Karl E. G. Richter, Frank S. Menniti, Yalda Shahriari

Computer Science Faculty Publications

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that leads to the gradual loss of motor control, typically resulting in paralysis and death within 3 to 5 years of diagnosis. ALS shares neuropathological and genetic associations with fronto-temporal dementia (FTD), a neurodegenerative condition primarily impacting cognitive functions. These two conditions are increasingly viewed as manifestations of a single molecular disease process that affects distinct brain systems, impacting motor neuronal pathways in ALS and fronto-cortical functions in FTD. However, this simple dichotomy belies the complexity of these conditions. In particular, patients with primary motor ALS can also experience significant cognitive …

Sage: Spatially Aware Gene Selection And Dual-View Embedding Fusion For Domain Identification In Spatial Transcriptomics, Yi He, Yunpei Xu, Liqing Ding, Hong-Dong Li, Yaohang Li, Shaokai Wang

Computer Science Faculty Publications

Despite enabling high-resolution mapping of gene expression within tissues, spatial transcriptomics (ST) still faces challenges in accurately segmenting spatial domains due to complex tissue architecture and limitations of current methods. Most approaches rely on local spatial priors, lack gene-level interpretability, and fall short in capturing structure-discriminative genes or long-range functional relationships, limiting their ability to resolve biologically meaningful architectures. We present Spatially Aware Gene selection and dual-view Embedding fusion (SAGE), a unified and reproducible framework for domain identification in spatial transcriptomics that combines topic-driven gene selection with dual-view embedding fusion to address these gaps. SAGE integrates non-negative matrix factorization (NMF)-based …

Quantum Machine Learning Models: Principles, Frameworks, And Computational Challenges, K. A. Jayabalaji, S. Venkata Anand, Dineshkumar Rajendran, Prasanta Chatterjee Biswas, Sardor Omonov, Rubaid Ashfaq

Computer Science Faculty Publications

Quantum machine learning (QML) has become an optimistic avenue of harnessing quantum computation in data-driven modeling, especially of issues with high dimensionality and complicated correlations. Current methods are generally based on fixed or over-parameterized quantum circuits, and hence restricted to scalability as well as unproductive optimization in real-world hardware. This chapter introduces a hybrid quantum-classical learning system that is adaptive and provides principled quantum data encoding, architecture-conscious variational circuit design and resource-optimal optimization. The technique is based on the concepts of quantum architecture search and subspace-preserving transformations to trade expressiveness with trainability, and discretize the quantum model into a classical …

Examining Inclusive Computing Education For Blind Students In India, Akshay Kolgar Nayak, Yash Prakash, Md Javedul Ferdous, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

The growing demand for computer professionals, driven by the expanding Information Technology industry, has led to numerous inclusive computing education efforts. These efforts have even included blind or visually-impaired (BVI) students, who are being increasingly encouraged to pursue education and a career in computing, despite the visually-oriented nature of the discipline. Extant literature has predominantly focused on identifying and addressing the accessibility barriers faced by BVI students to promote more inclusive learning environments. While few studies have also investigated the accessibility of computing education from the perspectives of BVI learners and instructors, these have been primarily situated in the Global …

The Privacy Paradox Of Llms: User Perceptions And The Reality Of Pii Leakage, Shuai Cheng, Haitao Xu, Shu Meng, Shuai Hao, Chuan Yue, Zhao Li

Computer Science Faculty Publications

Large language models (LLMs) are increasingly deployed, yet they introduce significant privacy risks by disclosing personally identifiable information (PII) during interactions. Although prior work has demonstrated the feasibility of extracting PII from LLMs, no comprehensive study has evaluated the actual extent of PII leakage across mainstream LLMs or investigated user perceptions, literacy, and behavioral responses to these risks. To address these gaps, we conduct a large-scale evaluation of PII leakage in popular LLMs, demonstrating that attackers can extract email addresses and phone numbers with high success rates. Through a mixed-methods study involving 20 interviews and 204 survey participants, we identify …

Close Enough To Know, Organized Enough To Respond: State Associations As First-Response Advocacy Networks, Jordan Atkinson

Iowa Journal of Communication

This essay argues that state communication associations are uniquely positioned to serve as first-response advocacy networks for the communication discipline. In a higher education climate shaped by declining public trust, program cuts, academic freedom concerns, enrollment pressures, and contingent labor, state associations can provide local forms of connection, support, and advocacy that complement the work of regional and national associations. Drawing attention to their proximity, relationships, and responsiveness, this essay considers how these associations can help members recognize emerging threats, share resources, coordinate responses, and communicate the public value of communication education. It also argues that effective advocacy requires more …

An Explainable Transformer Framework For Sentiment Analysis In Aviation Workforce Data, Sovon Chakraborty, Protiva Das, Fahmid Al Farid, Fuyad Hasan Bhoyan, Farig Yousuf Sadeque, Jia Uddin, Hezerul Abdul Karim

Computer Science Faculty Publications

Aviation is one of the predominant sectors that contribute significantly to the global economy. With the advent of technology, this industry is witnessing a paradigm shift towards data-driven approaches. The morale of the airline employees is barely noticed, which causes fatigue and depression. Furthermore, these mental health issues can be active reasons for destructive accidents. In this research, the authors are focused on collecting insightful information on aviation employees from Glassdoor.com. Moreover, the authors focus on analyzing the sentiments of the employees of renowned aviation companies. Primarily, the authors scraped necessary data from Glassdoor.com and created a dataset named JetJobJoy …

Maxgrnet: A Multi-Axis Vision Transformer With Improved Generalization For Eye Disease Classification Using Explainable Ai With Insertion-Deletion Operations On Fundus Images, Md Mehedi Hasan Santo, Fuyad Hasan Bhoyan, Fuad Ibne Jashim Farhad, Fahmid Al Farid, Sovon Chakraborty, Md Humaion Kabir Mehedi, Jia Uddin, Hezerul Bin Abdul Karim

Computer Science Faculty Publications

Eye diseases, including diabetic retinopathy (DR), glaucoma, and cataracts, represent a major global health concern and can lead to severe visual impairment or blindness if not identified in a timely manner. This study proposes a novel eye disease classification framework based on a multi-axis vision transformer (MaxViT) applied to color fundus images with Explainable Artificial Intelligence (XAI) techniques to enhance model transparency. The proposed architecture integrates transformer-based attention mechanisms with Global Response Normalization (GRN)-based multi-layer perceptron (MLP) layers to capture complex spatial and contextual relationships within fundus images effectively. The model was evaluated on a publicly available eye disease classification …

Biosecure-Llm Framework: Protecting Llms From Cyberbiosecurity Threats And The Case For Independent Ai Safety Governance, Xavier-Lewis Palmer, Lucas Potter, Srdjan Lesaja, Sotirios Karathanasis, Mohammad Ghasemigol

Computer Science Faculty Publications

Large Language Models (LLMs) are becoming critical infrastructure in scientific, healthcare, and governmental contexts. As frontier AI laboratories increasingly partner with government agencies, a fundamental question arises: Who should control the safety and policy-enforcement layers that constrain model behavior? Current safety mechanisms (LLM guardrails) are typically designed for generic "harmlessness" and operate by detecting semantic patterns and refusing requests. However, they are inadequate governance instruments because they cannot implement auditable, domain-specific controls tied to external regulatory policy objects (e.g., control lists or rules governing personally identifying information). Even a perfectly aligned model is not able to express institution-specific policy without …

Privacy-Preserving Federated Learning With Optimized Ensemble Weighting And Knowledge Distillation For Covid-19 Detection From Non-Iid Medical Imaging Data, Richard Annan, Hong Qin, Robert Newman, Madhuri Siddula, Letu Qingge

Computer Science Faculty Publications

Medical imaging enables rapid and accurate diagnosis of COVID-19, with CT scans proving especially effective. However, data privacy concerns limit collaborative model development across hospitals. To address this issue, we introduce a novel federated learning framework. It is referred to as Independent Knowledge Distillation with post-Ensemble Federated Learning (IKDEFL). Differential Privacy (DP) is integrated into the framework to improve privacy guarantees. Three DP mechanisms are evaluated. These include Fixed Gaussian, Gaussian Adaptive, and Tree Adaptive. The evaluation has been conducted on heterogeneous and Non-Independent and Identically Distributed (Non-IID) datasets. These datasets reflect real-world hospital scenarios. Results show that IKDEFL significantly …

An Investigation Of Federated Gnns Under Aggregation, Data Poisoning, And Differential Privacy For Icu Length-Of-Stay Prediction, Shakib Mahmud Dipto, Soumya Banerjee, Sandip Roy, Ahmad F. Al Musawi, Preetam Ghosh, Sachin Shetty, Pratip Rana

Computer Science Faculty Publications

Accurate prediction of ICU Length of Stay (LoS) is essential for clinical decision-making and healthcare resource management. Graph Neural Networks (GNNs), such as GraphSAGE, offer a natural fit by capturing patient data from Electronic Health Records (EHRs) through graph structures. However, the distributed and sensitive nature of this data raises both privacy and legal concerns regarding the aggregation and training of GNN models. This additionally leads to issues with data imbalance and model robustness. In this study, we perform an analysis of the Federated Graph Neural Network (GNN-FL) framework to enable decentralized learning on EHRs derived from the MIMIC-III dataset. …

Isotropic Shrinkage Of Patterned Vacancies Enables Three-Dimensional Nanoprecise Metastructures For Visible Light Applications, Quansan Yang, Gaojie Yang, Takahiro Nambara, Hiroyuki Kusaka, Yuichiro Kunai, Alex C. Matlock, Corban Swain, Brett Pryor, Yannick Salamin, Daniel Oran, Hasindu Kariyawasam, Ramith Hettiarachchi, Dushan Wadduwage, Marin Soljačić, Peter T. C. Soflaei, Edward S. Boyden

Computer Science Faculty Publications

Three-dimensional metastructures with nanoscale feature sizes exhibit unique properties compared with structures with larger feature sizes, but are difficult to fabricate. Here we introduce implosion carving (ImpCarv), a method for photopatterning vacancies of complex geometry throughout materials, followed by isotropic shrinkage (>10-fold). ImpCarv works by photoactivating sensitizers to generate reactive oxygen species that cleave a swollen hydrogel at defined points, followed by controlled shrinkage via dehydration. ImpCarv creates three-dimensional metastructures where the refractive index of each point throughout a material can be specified with nanoscale precision via material presence or absence. By leveraging refractive index programmability for precise phase …

Toward A Centralized Cross Domain Database For Reproducibility And Replicability Studies, Rochana R. Obadage, Sarah Rajtmajer, Jian Wu

Computer Science Faculty Publications

Reproducibility and replicability (R&R) are structural properties of scientific knowledge, yet existing R&R evidence remains fragmented across domains and initiatives. We present an ongoing effort to develop a centralized, cross-domain database of R&R studies that links published works to their corresponding R&R attempts and supporting assessments. Bibliographic metadata and title-based matching through scholarly indexing services identify canonical records and persistent identifiers. A heuristic, uncertainty-aware matching algorithm supports intra-source and inter-source deduplication, complemented by manual review of ambiguous cases. A unified schema accommodates heterogeneous assessment frameworks and records original studies, R&R studies, assessments, aggregated summaries, and source provenance. The database currently …

Replicatorbench: Benchmarking Llm Agents For Replicability In Social And Behavioral Sciences, Bang Nguyen, Dominik Soós, Qian Ma, Rochana R. Obadage, Zack Ranjan, Sai Koneru, Timothy M. Errington, Shakhlo Nematova, Sarah Rajtmajer, Jian Wu, Meng Jiang

Computer Science Faculty Publications

The literature has witnessed an emerging interest in developing and evaluating AI agents for automated assessment of research claims in scientific papers. Existing benchmarks focus primarily on the computational aspect of this task, testing agents' ability to reproduce or replicate research outcomes when having access to the code and data. This setting, while foundational, (1) fails to capture the inconsistent availability of new data for replication as opposed to reproduction, and (2) lacks ground-truth diversity by focusing exclusively on fully reproducible or replicable papers, thereby failing to evaluate an agent's ability to identify non-replicable research. Furthermore, most benchmarks only evaluate …

Exploit Machina, Andrea Matwyshyn

Faculty Scholarship

Over half a century ago, Hannah Arendt cautioned us to “think what we are doing” when we build new technologies. Engaging with her counsel and a set of historical case studies, this Article frames what it calls exploit machina problems. Exploit machina refers to situations where broken technologies and broken governance combine to irreparably harm the public. In other words, exploit machina involves organizational choices to knowingly leverage technology as part of legally problematic conduct, including various forms of fraud. In the language of data science, exploit machina situations implicate strategic decisions in building and managing artificial intelligence (AI); they …

Neuro-Advocacy: Harmonizing Persuasion With The Operation Of The Brain, Gary Gildin

Faculty Scholarship

The most significant contemporary advances in effective courtroom advocacy will emerge from what on the surface is an unlikely source: neuroscience. Revolutionary advances in technology for the first time allow us to see how the human brain makes decisions. The findings are wholly inconsistent with how the trial process presupposes the finder of fact will perceive and use information delivered by lawyers and witnesses. Consequently, lawyers seeking to persuade jurors (and judges) must reframe their advocacy to align with what we have learned about how the minds of these decision-makers will receive testimony and argument and use these inputs to …

George White Family Papers - Accession 1900, White Family

Manuscript Collection

This collection consists of bound volumes, family Bibles, lecture and financial notebooks, diaries, scrapbooks, photographs, magazines, publications, and memorabilia collected or owned by members of the White family and related families. The White family, descendants of George Pendleton and Ann Hutchinson White, was among the founding families of Rock Hill, South Carolina, and played a significant role in the development and history of the community. The family’s historical presence in Rock Hill is also reflected in the White Home, one of the early homes constructed in the city and now a local historic attraction, and White Street in downtown Rock …

David Bancroft Johnson Personal Papers - Accession 2, David Bancroft Johnson

Manuscript Collection

This collection consists of the personal correspondence and papers of Dr. David Bancroft Johnson, founder and first president of Winthrop.  The letters address various subjects and people, both personal and professional, concerning issues from the original funding of Winthrop in 1886 to the minutiae of ordering flour and cotton seed, as well as personal letters to family, friends, and acquaintances. This collection also includes news clippings, brochures, programs, and various documents belonging to Dr. Johnson.  The letters and documents are by organized by each item chronologically.

Crypto Exchanges In Market Regulation, Yesha Yadav

Vanderbilt Law Review

The cryptocurrency market has proven notoriously challenging to regulate despite a decade during which it has gained in economic size, sophistication, and cultural impact. Even as repeated crises have left ordinary savers wiped out, regulation has shown itself to be woefully absent as a means of mitigating risks ex ante or helping cover the cost of damage ex post. This Article looks to break this deadlock by proposing a tried-and-tested solution: It argues that regulators should task crypto exchanges with providing oversight as self-regulatory organizations of the crypto market in which they play an anchoring role. It offers three supporting …

Presidential Immunity: Before And After Trump, Thomas P. Schmidt

Vanderbilt Law Review

'All the officers of government," the Supreme Court said long ago, "from the highest to the lowest, are creatures of the law, and are bound to obey it." Despite that ringing and categorical language, however, the Court has held that the President-the "highest" and most powerful of all government officers-is "immune" from judicial oversight in several respects. Indeed, when the Court held last Term that former presidents are presumptively immune from criminal prosecution for officials acts, the dissenters warned: '`n every use of official power, the President is now a king above the law."

This Article unpacks the various presidential …

Final Annual Operations And Maintenance (O&M) Report: Butte Treatment Lagoon (Btl) System – 2023, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Near Real-Time Adaptive Isotropic And Anisotropic Image-To-Mesh Conversion For Cerebral Aneurysm Simulations, Kevin Garner, Chander Sadasivan, Nikos Chrisochoides

Computer Science Faculty Publications

This paper presents two performance optimization techniques for a mesh adaptation method that is designed to help streamline the discretization of complex vascular geometries within the numerical modeling process. This method is integrated into a pipeline with an image-to-mesh conversion tool to generate adaptive anisotropic meshes from segmented medical images. The pipeline is shown to satisfy quality, fidelity, smoothness, and robustness requirements while providing near real-time performance for medical image-to-mesh conversion. Tested with two brain aneurysm cases and utilizing up to 96 CPU cores within a single, multicore node on Purdue University’s Anvil supercomputer, the parallel adaptive anisotropic meshing method …

Adaptive Self-Attention For Enhanced Segmentation Of Adult Gliomas In Multi-Modal Mri, Evan P. Savaria, Jiangwen Sun

Computer Science Faculty Publications

Every year there are an estimated 80,000–90,000 new glioma cases, highlighting the need for reliable imaging-based decision support. Although deep learning has improved tumor sub-region segmentation, many state-of-the-art models fail to fully capture complementary information across T1, T1Gd, T2, and FLAIR MRI modalities and often operate as “black boxes,” limiting physician trust when precise delineation is critical for surgical planning, radiation targeting, and treatment monitoring. To address these limitations, we propose AIMS, an Adaptive Integrated Multi-Modal Segmentation framework that maintains modality-specific feature streams and employs adaptive self-attention within a hierarchical CNN-Transformer architecture to prioritize and fuse multi-modal MRI features. We …