Digital Commons Network^™

Civic Education And Democracy's Flaws, Robert L. Tsai

Faculty Scholarship

Today, liberalism and democracy are beset by competitors that seek to return power to religious traditionalists or partisans masquerading as civic republicans. In such an environment, can civic education do some good, and even help bridge our society’s deepening divides?

Seana Shiffrin has characteristically brought deep learning and penetrating insight to the project of civic education in a modern democracy. Against a “dominant” model of citizenship in which “citizens vote and hand off power to their representatives”— which she believes encourages the people to maintain an unhealthy distance from government— she proposes a richer account of political community in which …

Mitigating Algorithmic Bias: Strategies For Addressing Discrimination In Data, Sonia Gipson Rankin

Faculty Scholarship

This article examines the pervasive issue of algorithmic bias, particularly within large language models (LLMs) and the legal system. It argues that unlike simple programming bugs, these biases are deeply ingrained in the design and training data of artificial intelligence (AI) systems. By understanding the historical roots of bias and its realworld consequence across various sectors, we can develop effective strategies to mitigate its impact and ensure AI serves as a tool for progress. Weaving together historical insights, case studies, and forward-looking recommendations, the article aims to equip legal professionals with the knowledge and tools necessary to lead the charge …

Full Hill-Type Muscle Model Of The I1/I3 Retractor Muscle Complex In Aplysia Californica, Qianxue Chen, Jiayi Shen, Samantha Pao, Yu Huan, Gregory P. Sutton, Jeffery P. Gill, Hillel J. Chiel

Faculty Scholarship

The coordination of complex behavior requires knowledge of both neural dynamics and the mechanics of the periphery. The feeding system of Aplysia californica is an excellent model for investigating questions in soft body systems’ neuromechanics because of its experimental tractability. Prior work has attempted to elucidate the mechanical properties of the periphery by using a Hill-type muscle model to characterize the force generation capabilities of the key protractor muscle responsible for moving Aplysia’s grasper anteriorly, the I2 muscle. However, the I1/I3 muscle, which is the main driver of retractions of Aplysia’s grasper, has not been characterized. Because of the importance …

A Model Of Time-Dependent Macromolecular And Elemental Composition Of Phytoplankton, Anne Willem Omta

Faculty Scholarship

Phytoplankton Chl:C:N:P ratios are important from both an ecological and a biogeochemical perspective. We show that these elemental ratios can be represented by a phytoplankton physiological model of low complexity that includes major cellular macromolecular pools. In particular, our model resolves time-dependent intracellular pools of chlorophyll, proteins, nucleic acids, carbohydrates/lipids, and N and P storage. Batch culture data for two diatom and two prasinophyte species are used to constrain parameters that represent specific allocation traits and strategies. A key novelty is the simultaneous estimation of physiological parameters for two phytoplankton groups of such different sizes. The number of free parameters …

The Effect Of Early Postnatal Auditory Stimulation On Outcomes In Preterm Infants, Juliann M. Di Fiore, Gloria Liu, Kenneth A. Loparo, Cynthia F. Bearer

Faculty Scholarship

Abstract: Preterm infants are deprived of in utero sensory stimulation during the third trimester, an important period of central nervous system development. As a result, maturational trajectories are often reduced in infants born preterm. One such system affected is the brain including the auditory and respiratory control pathways. During normal pregnancy the intrauterine environment attenuates external auditory stimuli while exposing the fetus to filtered maternal voice, intra-abdominal sounds, and external stimuli. In contrast, during the third trimester of development, preterm infants are exposed to a vastly different soundscape including non-attenuated auditory sounds and a lack of womb related stimuli, both …

Extreme Value Statistics Analysis Of Process Defects In Additive Manufacturing Materials, Ayorinde E. Olatunde, Kristen Hernandez, Austin Ngo, Arafath Nihar, Thomas G. Ciardi, Rachel Yamamoto, Pawan K. Tripathi, Roger H. French, John J. Lewandowski, Anirban Mondal

Faculty Scholarship

Fatigue and fracture studies focused on process defects that occur in Additive Manufacturing (AM) materials have shown that defect populations possess features which are better measured with extreme value statistics (EVS). In AM alloys, defect occurrences increase with material volume. This situation facilitates the need to model process defects in the path of fatigue crack growth with suitable statistical tools, such as EVS, which is more cost-effective when compared to destructive experiments. The application of EVS on defect space features helps determine the difference in defects present on fracture surfaces. As the fatigue quality of any material depends on its …

Uhd: Unary Processing For Lightweight And Dynamic Hyperdimensional Computing, Mehran Shoushtari Moghadam, M. Hassan Najafi

Faculty Scholarship

Hyperdimensional computing (HDC) is a novel computational paradigm that operates on long-dimensional vectors known as hypervectors. The hypervectors are constructed as long bit-streams and form the basic building blocks of HDC systems. In HDC, hypervectors are generated from scalar values without considering bit significance. HDC is efficient and robust for various data processing applications, especially computer vision tasks. To construct HDC models for vision applications, the current state-of-the-art practice utilizes two parameters for data encoding: pixel intensity and pixel position. However, the intensity and position information embedded in high-dimensional vectors are generally not generated dynamically in the HDC models. Consequently, …

Image-Based Fracture Surface Defect Characterization Methods For Additively Manufactured Ti-6al-4v Tested In Fatigue, Austin Ngo, Kristen Hernandez, Ayorinde E. Olatunde, Thomas G. Ciardi, Alex Harding, Arafath Nihar, Anirban Mondal, Roger H. French, Laura S. Bruckman, John J. Lewandowski

Faculty Scholarship

Fatigue initiation in additively manufactured samples/parts often occurs at processed-induced defects such as lack-of-fusion (LoF), keyhole, or other morphological/microstructural defects that have unique characteristics and measurable qualities. Attempts at identifying and minimizing such defects have utilized optimized processing conditions along with in situ and ex situ characterization that includes metallography and/or X-ray computed tomography (XCT). This paper highlights the benefits of using fracture surface analyses to detect and quantify defects that may not be detected by metallography/XCT due to sectioning and resolution limits. In addition to using manual quantification of fatigue initiating LoF and keyhole defects on fracture surfaces, image-based …

Multiple Changes In Connectivity Between Buccal Ganglia Mechanoafferents And Motor Neurons With Different Functions After Learning That Food Is Inedible In Aplysia, Hillel J. Chiel, Jeffery Gill

Faculty Scholarship

Changes caused by learning that a food is inedible in Aplysia were examined for fast and slow synaptic connections from the buccal ganglia S1 cluster of mechanoafferents to five followers, in response to repeated stimulus trains. Learning affected only fast connections. For these, unique patterns of change were present in each follower, indicating that learning differentially affects the different branches of the mechanoafferents to their followers. In some followers, there were increases in either excitatory or inhibitory connections, and in others, there were decreases. Changes in connectivity resulted from changes in the amplitude of excitation or inhibition, or as a …

Perceptions And Aspirations Of Undergraduate Computer Science Students Towards Generative Ai: A Qualitative Inquiry, James Hutson, Theresa Jeevanjee

Faculty Scholarship

This article presents a comprehensive study conducted during the spring semester of 2024, aimed at exploring undergraduate computer science students’ perceptions, awareness, and understanding of generative artificial intelligence (GAI) tools within the context of their Artificial Intelligence (AI) courses. The research methodology employed qualitative techniques, including human-subject research and focus groups, to delve into students’ insights on the evolution of AI as delineated in the seminal textbook by Russell and Norvig. The study-initiated discussions on the historical development of AI, prompting students to reflect on the aspects that intrigued them the most, and to identify which historical concepts and methodologies, …

Predictive Power Of Machine Learning Models On Degree Completion Among Adult Learners, Emily Barnes, James Hutson, Karriem Perry

Faculty Scholarship

The integration of machine learning (ML) into higher education has been recognized as a transformative force for adult learners, a growing demographic facing unique educational challenges. This study evaluates the predictive power of three ML models—Random Forest, Gradient-Boosting Machine, and Decision Trees—in forecasting degree completion among this group. Utilizing a dataset from the academic years 2013-14 to 2021-22, which includes demographic and academic performance metrics, the study employs accuracy, precision, recall, and F1 score to assess the efficacy of these models. The results indicate that the Gradient-Boosting Machine model outperforms others in predicting degree completion, suggesting that ML can significantly …

Is John Stuart Mill's On Liberty Obsolete?, Vincent A. Blasi

Faculty Scholarship

In On Liberty, published in 1859, John Stuart Mill argues for the “absolute” protection of the “liberty of thought and discussion.” Ever the empiricist, he maintains that such uncompromised freedom, not for all communication or self-expression but for the subset of those activities that qualifies as thought and discussion, would generate the best overall consequences for societies such as Great Britain and the United States. The advent of digital technology has altered how thought and discussion is generated, distributed, and received in ways that might problematize some of the empirical assumptions upon which Mill's argument in On Liberty is …

Navigating The Ethical Terrain Of Ai In Higher Education: Strategies For Mitigating Bias And Promoting Fairness, Emily Barnes, James Hutson

Faculty Scholarship

Artificial intelligence (AI) and machine learning (ML) are transforming higher education by enhancing personalized learning and academic support, yet they pose significant ethical challenges, particularly in terms of inherent biases. This review critically examines the integration of AI in higher education, underscoring the dual aspects of its potential to innovate educational paradigms and the essential need to address ethical implications to avoid perpetuating existing inequalities. The researchers employed a methodological approach that analyzed case studies and literature as primary data collection methods, focusing on strategies to mitigate biases through technical solutions, diverse datasets, and strict adherence to ethical guidelines. Their …

Present Case Studies Highlighting Practical Implications Of Architectural Design Choices, Emily Barnes, James Hutson

Faculty Scholarship

The interpretability of deep neural networks (DNNs) has become a crucial focus within artificial intelligence and machine learning, particularly as these models are increasingly used in high-stakes applications such as healthcare, finance, and autonomous driving. This article explores the impact of architectural design choices on the interpretability of DNNs, emphasizing the importance of transparency, trust, and accountability in AI systems. By presenting case studies and experimental results, the article highlights how different architectural elements—such as layer types, network depth, connectivity patterns, and attention mechanisms—affect model interpretability and performance. The discussion is structured into three main sections: real-world applications, architectural trade-offs, …

Repeated Stimulation Of Feeding Mechanoafferents In Aplysia Generates Responses Consistent With The Release Of Food, Hillel J. Chiel

Faculty Scholarship

How does repeated stimulation of mechanoafferents affect feeding motor neurons? Monosynaptic connections from a mechanoafferent population in the Aplysia buccal ganglia to five motor followers with different functions were examined during repeated stimulus trains. The mechanoafferents produced both fast and slow synaptic outputs, which could be excitatory or inhibitory. In contrast, other Aplysia mechanoafferents produce only fast excitation on their followers. In addition, patterns of synaptic connections were different to the different motor followers. Some followers received both fast excitation and fast inhibition, whereas others received exclusively fast excitation. All followers showed strong decreases in fast postsynaptic potential (PSP) amplitude …

Strategic Integration Of Ai In Higher Education And Industry: The Ai8-Point Model, Emily Barnes, James Hutson

Faculty Scholarship

The AI8-Point Model, derived from extensive experience in technology, AI, and higher education administration, addresses the critical need for cost-effective, high-impact strategies tailored to higher education. Despite the transformative potential of AI in enhancing student engagement, optimizing processes, and improving educational outcomes, institutions often struggle with practical implementation. The AI8-Point Model fills this gap by offering strategies that balance cost and impact. Visualized as a circle divided into four quadrants, the model encompasses phases of student engagement and institutional interaction: pre-enrollment beyond institutional control, pre-enrollment within institutional control, post-enrollment within institutional control, and post-enrollment beyond institutional control. Each quadrant contains …

Rays Of Hope For The Administrative State, Peter L. Strauss

Faculty Scholarship

The assault on Chevron has not yet been resolved, but it is next to impossible to believe that the sensible Justices of the Court will go any further than to reaffirm that deciding questions of the scope of agency authority is exclusively for judicial resolution (though perhaps with Skidmore-like attention to the views of the agency, so much more familiar with its statutes as a whole). This was the position clearly taken by the Court in 1940, in United States v. American Trucking Ass’ns., and to go beyond it into decision, rather than review, of agency judgments would …

Interview With David Webber: Reflections On The Past, Present, And Future Of Labor's Capital, Alvin Velazquez, David H. Webber

Faculty Scholarship

ALVIN VELAZQUEZ: Good afternoon! My name is Alvin Velazquez. I am an Associate General Counsel at the Service Employees International Union (SEIU), where for the last fifteen years I have given advice on bankruptcy, corporate law matters, and tech matters. I’m also an adjunct professor at Georgetown Law School where I have taught employment law and am teaching a seminar called Systemic Racism, Colonialism, and Bankrupt Governments.1 Before arriving at SEIU I worked as a litigation associate at several large law firms. I also admit, rather sheepishly, that I am one of the few graduates of Cornell in the labor …

The Subfederal In Immigration Polarization, Huyen Pham, Pham Hoang Van

Faculty Scholarship

The framing of subfederal immigration regulation as a red- blue divide is conventional wisdom. As more states, cities, and counties have engaged in the regulation of immigrants within their jurisdictions, it is not particularly surprising to see deep-red states like Texas enacting laws that restrict the rights of immigrants in their jurisdictions (e.g., requiring police within the state to honor detainers issued by United States Immigration and Customs Enforcement (ICE)) or deep-blue states like California enacting laws that protect immigrants’ rights (e.g., issuing driver’s licenses without requiring proof of lawful immigration status).

Rather than only reflecting national polarization on immigration …

Architectural Elements Contributing To Interpretability Of Deep Neural Networks (Dnns), Emily Barnes, James Hutson

Faculty Scholarship

The interpretability of Deep Neural Networks (DNNs) has become a critical focus in artificial intelligence and machine learning, particularly as DNNs are increasingly used in high-stakes applications like healthcare, finance, and autonomous driving. Interpretability refers to the extent to which humans can understand the reasons behind a model's decisions, which is essential for trust, accountability, and transparency. However, the complexity and depth of DNN architectures often compromise interpretability as these models function as "black boxes." This article reviews key architectural elements of DNNs that affect their interpretability, aiming to guide the design of more transparent and trustworthy models. The primary …

Evaluating Methods For Assessing Interpretability Of Deep Neural Networks (Dnns), Emily Barnes, James Hutson

Faculty Scholarship

The interpretability of deep neural networks (DNNs) is a critical focus in artificial intelligence (AI) and machine learning (ML), particularly as these models are increasingly deployed in high-stakes applications such as healthcare, finance, and autonomous systems. In the context of these technologies, interpretability refers to the extent to which a human can understand the cause of a decision made by a model. This article evaluates various methods for assessing the interpretability of DNNs, recognizing the significant challenges posed by their complex and opaque nature. The review encompasses both quantitative metrics and qualitative evaluations, aiming to identify effective strategies that enhance …

Navigating The Complexities Of Ai: The Critical Role Of Interpretability And Explainability In Ensuring Transparency And Trust, Emily Barnes, James Hutson

Faculty Scholarship

The interpretability and explainability of deep neural networks (DNNs) are paramount in artificial intelligence (AI), especially when applied to high-stakes fields such as healthcare, finance, and autonomous driving. The need for this study arises from the growing integration of AI into critical areas where transparency, trust, and ethical decision-making are essential. This paper explores the impact of architectural design choices on DNN interpretability, focusing on how different architectural elements like layer types, network depth, connectivity patterns, and attention mechanisms affect model transparency. Methodologically, the study employs a comprehensive review of case studies and experimental results to analyze the balance between …

A Data Integration Framework Of Additive Manufacturing Based On Fair Principles, Kristen J. Hernandez, Erika I. Barcelos, Jayvic C. Jimenez, Arafath Nihar, Pawan K. Tripathi, Roger H. French, Laura S. Bruckman

Faculty Scholarship

Abstract: Laser-powder bed fusion (L-PBF) is a popular additive manufacturing (AM) process with rich data sets coming from both in situ and ex situ sources. Data derived from multiple measurement modalities in an AM process capture unique features but often have different encoding methods; the challenge of data registration is not directly intuitive. In this work, we address the challenge of data registration between multiple modalities. Large data spaces must be organized in a machine-compatible method to maximize scientific output. FAIR (findable, accessible, interoperable, and reusable) principles are required to overcome challenges associated with data at various scales. FAIRified data …

Exploring 2d X-Ray Diffraction Phase Fraction Analysis With Convolutional Neural Networks: Insights From Kinematic-Diffraction Simulations, Weiqi Yue, Mohommad Redad Mehdi, Pawan K. Tripathi, Matthew A. Willard, Frank Ernst, Roger H. French

Faculty Scholarship

Deep-learning models are effective for analyzing the complex information in 2D X-ray diffraction (XRD) patterns. Accurately collecting parameters of the material sample is crucial during model training, significantly impacting model performance. In this study, we employ a kinematic-diffraction simulator to generate simulated 2D XRD patterns for Ti–6Al–4V alloy, allowing precise control of sample parameters. These simulated patterns are used to train convolutional neural networks, predicting β-phase volume fractions. The training data set consists exclusively of 2D XRD patterns with pure α- or pure β-phase, while the testing set incorporates patterns with intermediate phase volume fraction. In particular, we investigate how …

Exploring 2d X-Ray Diffraction Phase Fraction Analysis With Convolutional Neural Networks: Insights From Kinematic-Diffraction Simulations, Weiqi Yue, Mohommad Redad Mehdi, Pawan K. Tripathi, Matthew A. Willard, Frank Ernst, Roger H. French

Faculty Scholarship

Deep-learning models are effective for analyzing the complex information in 2D X-ray diffraction (XRD) patterns. Accurately collecting parameters of the material sample is crucial during model training, significantly impacting model performance. In this study, we employ a kinematic-diffraction simulator to generate simulated 2D XRD patterns for Ti–6Al–4V alloy, allowing precise control of sample parameters. These simulated patterns are used to train convolutional neural networks, predicting β-phase volume fractions. The training data set consists exclusively of 2D XRD patterns with pure α- or pure β-phase, while the testing set incorporates patterns with intermediate phase volume fraction. In particular, we investigate how …

Broad-Area-Search Of New Construction Using Time Series Analysis Of Landsat And Sentinel-2 Data, Xiaojing Tang, Madison G. Barrett, Kangjoon Cho, Kelsee H. Bratley, Katelyn Tarrio, Yingtong Zhang, Hanfeng Gu, Peter Rasmussen, Marc Bosch, Curtis E. Woodcock

Faculty Scholarship

New construction activities can alter surface albedo and structure, which then affect surface temperature and roughness, and hence have a significant impact on urban climate. Construction activity is also an important indicator of human development and movement and is of high interest to the intelligence community. A new approach for Broad-Area-Search of New Construction activities (BASC) by combining time series analysis and rule-based filters using Landsat data was developed and tested in five selected cities (Boston, Shanghai, Sa ̃o Paulo, Dubai, and Ho Chi Minh City). The algorithm transforms Landsat images into fractions of a set of four endmembers using …

Continuing Benefits Of The Montreal Protocol And Protection Of The Stratospheric Ozone Layer For Human Health And The Environment, Laura S. Bruckman

Faculty Scholarship

The protection of Earth’s stratospheric ozone (O3) is an ongoing process under the auspices of the universally ratified Montreal Protocol and its Amendments and adjustments. A critical part of this process is the assessment of the environmental issues related to changes in O3. The United Nations Environment Programme’s Environmental Effects Assessment Panel provides annual scientific evaluations of some of the key issues arising in the recent collective knowledge base. This current update includes a comprehensive assessment of the incidence rates of skin cancer, cataract and other skin and eye diseases observed worldwide; the effects of UV radiation on tropospheric oxidants, …

A Turn-Off Fluorescent Sensor For Metal Ions Quantifies Corrosion In An Organic Solvent, Lianlian Liu, Zechariah Pfaffenberger, Mark Siegel, Anuj Saini, Lydia Kisley

Faculty Scholarship

We demonstrate that the corrosion of AISI 1045 medium carbon steel and pure aluminum can be quantified by the turn-off fluorescent sensor Phen Green-SK (PGSK) in ethanol-based solutions. We first evaluate the dependence of the chelation enhanced quenching of PGSK on iron and aluminum ion concentrations. Subsequently, we apply PGSK to examine the anodic dissolution of metal corrosion. The observed time-dependent PGSK-quenching quantifies the corrosion rates of two metals over 24 h of immersion in ethanol-based solutions. The PGSK-based quantification of corrosion is compared to scanning electron microscopy and electrochemical techniques, including open circuit potential and Tafel extrapolation. The corrosion …

Nodes For Modes: Nodal Honeycomb Metamaterial Enables A Soft Robot With Multimodal Locomotion, Yusuf Dikici, Kathryn Daltorio, Ozan Akkus

Faculty Scholarship

Soft-bodied animals, such as worms and snakes, use many muscles in different ways to traverse unstructured environments and inspire tools for accessing confined spaces. They demonstrate versatility of locomotion which is essential for adaptation to changing terrain conditions. However, replicating such versatility in untethered soft-bodied robots with multimodal locomotion capabilities have been challenging due to complex fabrication processes and limitations of soft body structures to accommodate hardware such as actuators, batteries and circuit boards. Here, we present MetaCrawler, a 3D printed metamaterial soft robot designed for multimodal and omnidirectional locomotion. Our design approach facilitated an easy fabrication process through a …

Simulacra And Historical Fidelity In Digital Recreation Of Lost Cultural Heritage: Reconstituting Period Materialities For The Period Eye, Trent Olsen, James Hutson, Charles O'Brien, Jeremiah Ratican

Faculty Scholarship

The advancement of digital technologies in art history has opened avenues for reconstructing lost or damaged cultural heritage, a need highlighted by the deteriorated state of many artworks from the 1785 Salon. Grounded in the concept of the “Period Eye” by art historian Michael Baxandall, which emphasizes understanding artworks within their original historical and cultural contexts, this study proposes a subfield focused on Reconstituting Period Materialities for the Period Eye. This methodology bridges comprehensive historical research with generative visual artificial intelligence (AI) technologies, facilitating the creation and immersive virtual reality viewing of artworks. Beyond mere visual replication, the approach aims …