Digital Commons Network^™

On Entityness And Takeovers: Acquisition Valuation, Theory Of The Firm, And Coase’S Error, Robert J. Rhee

Vanderbilt Law Review

This Article advances a theory of entityness that theorizes the firm and its relationship to the acquisition premium. This theory is the first scholarly analysis to construct a general model of takeover valuation by integrating the modern finance theory of asset value and a corrected Coasean theory of the firm. The acquisition premium is an enigma. Acquirers must pay it. But why? Isn’t the market price tethered to fundamental value through an efficient market? This enigma reveals a key insight about firms. The theory of entityness postulates that the acquisition premium is compensation for a capitalized asset intrinsic in the …

Original Discontent, William Magnuson

Vanderbilt Law Review

There are many theories of constitutional interpretation. Most, but not all, assert that, in interpreting the U.S. Constitution’s provisions, we should start by taking seriously the intentions of the enactors, meaning, roughly speaking, its drafters, defenders, and ratifiers. This Article argues that, in doing so, judges, scholars, and policymakers have underestimated an important feature of the process of constitution-making: the discontent of the enactors themselves with the Constitution they were enacting. Time and again, during the Philadelphia Convention of 1787, during the penning of the Federalist Papers, and during the state ratifying conventions, the enactors expressed deep reservations about the …

An Analytical Review Of Preprocessing Techniques In Bengali Natural Language Processing, Sovon Chakraborty, Protiva Das, Shakib Mahmud Dipto, Md Aktaruzzaman Pramanik, Jannatun Noor

Computer Science Faculty Publications

Research in Bengali Natural Language Processing (BNLP) is rapidly expanding. Despite being one of the most widely spoken languages in the world, BNLP research remains insufficient, particularly in Bengali speech recognition. The languages rich morphology, agglutinative structure, and diverse dialects make text and speech processing especially challenging. However, these challenges can be addressed with effective preprocessing techniques. Various organizations in Bangladesh and West Bengal are integrating Natural Language Processing (NLP) into their services, but without a thorough understanding of preprocessing, these implementations remain incomplete. Applying proper preprocessing techniques to the Bengali language will serve as a foundation for developing robust …

Not Here, Go There: Analyzing Redirection Patterns On The Web, Kritika Garg, Sawood Alam, Dietrich Ayala, Michele C. Weigle, Michael L. Nelson

Computer Science Faculty Publications

URI redirections are integral to web management, supporting structural changes, SEO optimization, and security. However, their complexities affect usability, SEO performance, and digital preservation. This study analyzed 11 million unique redirecting URIs, following redirections up to 10 hops per URI, to uncover patterns and implications of redirection practices. Our findings revealed that 50% of the URIs terminated successfully, while 50% resulted in errors, including 0.06% exceeding 10 hops. Canonical redirects, such as HTTP to HTTPS transitions, were prevalent, reflecting adherence to SEO best practices. Non-canonical redirects, often involving domain or path changes, highlighted significant web migrations, rebranding, and security risks. …

Benchmarking Batch-Effect Correction Methods Towards The Construction Of A Triple-Negative Breast Cancer Cell Atlas, Peter Scheible, Amy H. Tang, Jing He, Jiangwen Sun

Computer Science Faculty Publications

Triple-negative breast cancer (TNBC) requires detailed cellular mapping given its aggressive nature, immense tumor heterogeneity and genetic diversity. We integrated 156,794 cells from six scRNA-seq datasets—including tumors, metastases, and cell lines—to build a TNBC scRNA cell atlas, focusing on batch effect mitigation while maintaining biological and molecular details. Preprocessing f ilters noise, normalizes data, and leverages PCA for integration readiness. We utilized scANVI, a semi-supervised tool, to align datasets, preserving TNBC’s complex tumor heterogeneity via marker annotations [1]. UMAPs demonstrate biological clustering in integrated data, contrasted with datasetdriven unintegrated patterns. Assessments verifying effective batch correction. This method aligns with NASA’s …

Human Perception Of Ai Capabilities At Classifying Perturbed Roadway Signs, Katherine R. Garcia, Jing Chen, Yanru Xiao, Scott Mishler, Cong Wang, Bin Hu

Computer Science Faculty Publications

Artificial Intelligence (AI) is crucial to numerous functions required for driving automation systems, including the computer vision techniques used to detect the roadway environment and make real-time decisions. However, the images used as inputs to the AI system may be maliciously perturbed, or manipulated, causing the AI system to make an incorrect classification. In this study, we examined humans’ perception of the AI’s computer vision capability of classifying various road sign images, including the original images, images with two different types of malicious attacks, and images that are scrambled randomly at the pixel level. Our results showed that participants rated …

A Real-Time Approach To Capture Ambient And Focal Attention In Visual Search, Gavindya Jayawardena, Yasith Jayawardana, Yasasi Abeysinghe, Bhanuka Mahanama, Sampath Jayarathna, Jacek Gwizdka

Computer Science Faculty Publications

During visual search, individuals’ attention shifts between ambient and focal states in response to task demands and stimuli. The ambient/focal coefficient K is a statistically validated measure of these states, computed offline from fixation duration and saccade amplitude data. While current methods compute K offline, real-time computation could enable applications such as monitoring user attention, creating attention-adaptive user interfaces, and optimizing graphics rendering. However, real-time computation of K requires stable estimates for the parameters of fixation duration and saccade amplitude distributions. Since these distributions are heavy-tailed, the real-time estimates exhibit high variance and slow convergence. To overcome this, we propose …

Multi-Modal Mri Based Segmentation Of Brain Metastases Using Adaptive Self-Attention, Evan Savaria

Computer Science Faculty Publications

Brain metastases (BMs) are the most common adult central nervous system malignancy, affecting 20–40% of cancer patients. Accurate segmentation of metastatic lesions in multi-modal MRI is essential for treatment planning and prognosis however, manual delineation is time consuming and prone to variability. Traditional deep learning models such as U-Net, have improved segmentation accuracy but capture limited long-range dependencies and struggle with variations in metastasis size, shape, and distribution. This study introduces the Adaptive Integrated Multi-modal Segmentation (AIMS) model, an adaptive self-attention framework within a hybrid U-Net and Transformer architecture to enhance BM segmentation by leveraging multi-modal MRI integration. The proposed …

Ai For Nuclear Physics: The Exclaim Project, S. Liuti, D. Adams, M. Boër, G. W. Chern, M. Cuic, M. Engelhardt, G. R. Goldstein, B. Kriesten, Y. Li, H. W. Lin, M. Sievert, D. Sivers

Computer Science Faculty Publications

An overview of the recent activity of the newly funded EXCLusives with AI and Machine learning (EXCLAIM) collaboration is presented. The main goal of the collaboration is to develop a framework to implement AI and machine learning techniques in problems emerging from the phenomenology of high energy exclusive scattering processes from nucleons and nuclei, maximizing the information that can be extracted from various sets of experimental data, while implementing theoretical constraints from lattice QCD. A specific perspective embraced by EXCLAIM is to use the methods of theoretical physics to understand the working of ML, beyond its standardized applications to physics …

A Survey On Deep Learning For Drug-Target Binding Prediction: Models, Benchmarks, Evaluation, And Case Studies, Kusal Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Conventional drug discovery is expensive, time-consuming, and prone to failure. Artificial intelligence has become a potent substitute over the last decade, providing strong answers to challenging biological issues in this field. Among these difficulties, drug-target binding (DTB) is a key component of drug discovery techniques. In this context, drug-target affinity and drug–target interaction are complementary and essential frameworks that work together to improve our comprehension of DTB dynamics. In this work, we thoroughly analyze the most recent deep learning models, popular benchmark datasets, and assessment metrics for DTB prediction. We look at the paradigm shift in the development of drug …

Accessmenu: Enhancing Usability Of Online Restaurant Menus For Screen Reader Users, Nithiya Venkatraman, Akshay Kolgar Nayak, Suyog Dahal, Yash Prakash, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Online food ordering has become commonplace due to its convenience. The wide variety of culinary choices, combined with fast and economical door-delivery services, encourages more people to order food online. To facilitate this process, food vendors, including restaurants, often provide full menus on their websites, typically in visual formats such as images or PDFs. While this is convenient for sighted users, blind and visually impaired (BVI) individuals face significant challenges accessing these visual menus with their screen reader assistive technology. An interview study with 12 BVI screen reader users revealed that present assistive tools do not adequately satisfy the needs …

Deepssetracer 2.0: Improved Deep Learning Model Performance For Protein Secondary Structure Segmentation From Cryo-Em Maps, Bryan Hawickhorst, Thu Nguyen, Willy Wriggers, Jiangwen Sun, Jing He

Computer Science Faculty Publications

DeepSSETracer is a method for segmenting protein secondary structure from medium-resolution (5-10Å) cryogenic electron microscopy (cryo-EM) density maps. We conducted experiments and ablation studies to examine the effects of normalization methods, max-pooling, activation functions, and loss calculation region on DeepSSETracer. By combining multiple technical improvements, the performance of the new version, DeepSSETracer 2.0, was significantly enhanced compared to DeepSSETracer 1.1. On a set of 77 test cases, the weighted average per-voxel F1 score increased from 62.1% to 70.3% for helix detection, and from 47.8% to 62.5% for β-sheet detection. While each of the five modifications in the network enhanced the …

Benchmarking And Improving Foundation Model Dietary Estimates From Meal Images, Yongcheng Mu, Jiangwen Sun, Jing He

Computer Science Faculty Publications

Accurate quantifying dietary contents, such as calories, proteins, carbohydrates, and fats, from an image of a meal plate is vital for managing diabetes. Recently, Large Multimodal Models (LMMs) have excelled in complex vision-language tasks due to their use of very large, highly diverse data. This study benchmarked the use of seven LMMs that include full and lightweight models of GPT, Gemini, and Llama for nutrition estimation based on Google's Nutrition5k dataset and our own phone-collected DonateAndLearn dataset. We analyzed the performance of LMMs and the RGB-D fusion model, in which the RGB-D model was specifically trained using Nutrition5k data. On …

Effective Pii Extraction From Llms Through Augmented Few-Shot Learning, Shuai Cheng, Shu Meng, Haitao Xu, Haoran Zhang, Shuai Hao, Chuan Yue, Wenrui Ma, Meng Han, Fang Zhang, Zhao Li

Computer Science Faculty Publications

Large Language Models (LLMs) exhibit strong natural language processing capabilities but also pose significant privacy risks, particularly regarding the leakage of Personally Identifiable Information (PII) embedded in their training data. Existing PII extraction methods suffer from the limitations of low success rates or impracticality for large-scale PII extraction. In this study, we propose a novel PII extraction approach based on enhanced few-shot learning techniques, which achieves efficient and cost-effective PII retrieval without relying on fine-tuning or jailbreaking. We evaluated our approach on both open-source and closed-source LLMs. The experimental results demonstrate that, for non-targeted PII extraction, the attack success rate …

An Optimized Generalized Multi-Color Point Implicit Solver For Intel Gpus Using Oneapi Esimd, Joseph Wassell, Mohammad Zubair, Aaron Walden, Gabriel Nastac, Eric Nielsen, Timothée Ewart

Computer Science Faculty Publications

This paper presents an efficient implementation of a linear-solver kernel relevant to FUN3D, a suite of computational fluid dynamics software developed at NASA’s Langley Research Center. The linear solver is optimized for a range of block sizes commonly used in FUN3D. The implementation targets Aurora, the Argonne Leadership Computing Facility’s (ALCF) exascale machine featuring Intel Data Center Max 1550 GPUs. The linear solver’s performance is memory bandwidth-bound due to its low arithmetic intensity. The primary performance challenges stem from variable matrix row lengths and indirect memory access patterns inherent in unstructured-grid applications. Variable block sizes introduce additional complexity through differing …

Characterizing Language Use In Online Accessibility Discussion Forums, Nithiya Venkatraman, Anand Ravi Aiyer, Yash Prakash, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Discussion forums are one of the favored platforms for knowledge sharing. Given their popularity, copious research exists on understanding the linguistic and behavioral characteristics of forum conversations, so as to inform the design of many downstream applications including discourse visualization, sentiment analysis, and question answering. However, prior investigations have mainly focused on general forums designed primarily for sighted users, and as such the applicability of their findings to dedicated accessibility discussion forums frequented by blind screen reader users remains unanswered. To bridge this knowledge gap and facilitate the development of better-informed assistive technologies for blind people, we investigated language use …

Contextual Memory Recall: A Novel Metric For Class Incremental Learning, Balasubramanian S, Sai Subramaniam M., Sai Sriram Talasu, Yedu Krishna P., Pranav Phanindra Sai M., Darshan Gera, Ravi Mukkamala

Computer Science Faculty Publications

We propose a novel metric for class incremental learning (CIL) called Contextual Memory Recall (CMR), which evaluates how well a CIL model recalls previously learned classes when given relevant past cues. Inspired by human memory, CMR offers newer insights into continual aspects of a CIL model that were not addressed by previously proposed metrics for CIL. Specifically, the standard metric, average incremental accuracy (AIA), overlooks the quality of evolving feature representations, whereas our proposed CMR accounts for it. As a result, methods using feature distillation perform well under AIA but poorly under CMR, while those without feature distillation excel under …

Keynote Address For The 2025 University Of The Pacific Law Review Symposium: The Crime Victims' Rights Movement: Historical Foundations, Modern Ascendancy, And Future Aspirations, Paul Cassell

Utah Law Faculty Scholarship

This article, which serves as the keynote address for the 2025 University of Pacific Law Review Symposium, explores the past, present, and future of America's crime victims' rights movement-one of the most successful social movements in modern history.

Historically, crime victims played a central role in criminal justice processes through private prosecutions-i.e., the ability of victims to initiate or participate in criminal prosecutions. Today, while private prosecutions have been largely supplanted by public prosecutions, the victims' rights movement has successfully restored the victims' voice in criminal processes. The movement has reformed contemporary American criminal justice so that criminal processes now …

The Reality Of The Good Faith Exception, Matthew Tokson, Michael Gentithes

Utah Law Faculty Scholarship

The Fourth Amendment’s primary remedy is the exclusion of unlawfully obtained evidence at trial. But not every defendant whose rights are violated gets a remedy. The most substantial obstacle for defendants is the good faith exception, which directs courts to admit unlawfully collected evidence if the police can show they relied in good faith on existing authority. If the police rely on a statute that turns out to be unconstitutional, or a warrant or precedent that turns out to be invalid, the evidence they obtain will nonetheless be admitted under the good faith exception. The Supreme Court has justified this …

Loper Bright’S Deregulatory Synergies, Renee Farmer, Daniel G. Aaron

Utah Law Faculty Scholarship

For over 40 years, Chevron U.S.A. v. National Resources Defense Council stood as a keystone of administrative law. It infused agencies with the discretion to leverage the edges of statutory language and granted them the security that day-to-day agency actions would survive judicial review. Although Chevron’s demise in Loper Bright Enterprises v. Raimondo elicited rebuke from many legal scholars, it struck some as unsurprising, unimportant, or unconcerning, whether due to the narrowing of Chevron over time, the Supreme Court’s disuse of it in recent years, or the belief that its holding flew in the face of the Administrative Procedure Act. …

Discriminatory Censorship Laws, Jonathan Feingold, Joshua Weishart

Faculty Scholarship

The summer of 2020 ignited global protests for racial justice. Across the United States, millions marched with a modest plea: that America reckon with its racism. For K-12 schools, this moment pushed local communities and district leaders to create more inclusive classrooms and curricula. Yet before the summer had ended, America's antiracist turn provoked a backlash campaign that has proven far more impactful and enduring.

This campaign has featured the rise and spread of "discriminatory censorship laws"-a term we apply to government action designed to demean inclusionary values and to deny students access to critical knowledge, inquiry, and thinking. As …

Law As A Lamp Post, Janet Freilich

Faculty Scholarship

Law produces all manner of public information: court documents, securities filings, patents, property records, and much more. This information is used in a multitude of ways—it teaches readers about individual cases, transactions, or entities, and is also aggregated to inform policymaking, set priorities, and drive predictive analytics and artificial intelligence.

But choices about the information produced (or hidden) by law are often unintentional. Doctrines and institutions that appear facially unrelated to information production—like subject matter jurisdiction—nonetheless affect the shape and quantity of data produced. And even doctrines focused on information— like property recordation—create data used for purposes never envisioned by …

Gig Work At What Cost? Exploring Privacy Risks Of Gig Work Platform Participation In The U.S., Amogh Pradeep, Johanna Gunawan, Álvaro Feal, Woodrow Hartzog, David Choffnes

Faculty Scholarship

In recent years, "gig work" platforms have gained popularity as a way for individuals to earn money; as of 2021, 16% of Americans have at some point earned money from such platforms. Despite their popularity and their history of unfair data collection practices and worker safety, little is known about the data collected from workers (and users) by gig platforms and about the privacy dark pattern designs present in their apps.

This paper presents an empirical measurement of 16 gig work platforms' data practices in the U.S. We analyze what data is collected by these platforms, and how it is …

How Theories Of Art Can Inform Debates About Ai, Jessica Silbey

Faculty Scholarship

Debates about artificial intelligence (AI) tend to swing between the optimistic and the apocalyptic. I propose a less binary approach that frames conversations about AI from the perspectives of theories of art and creativity. Whether we agree that AI is artificial or intelligent, whether it should be constrained or liberated, we cannot deny its influence on literary, artistic, and innovative production. AI may be described as simply a new tool to produce art and science, like the camera or the microscope, or it may transform art and science, the way the internet transformed global communication. Either way, these debates about …

Consumer Agents, Rory Van Loo

Faculty Scholarship

In the twenty-first century economy, individuals need market help that they are not getting. The technology has long existed for a browser plug-in that would filter out toxic social media content or a virtual shopping assistant that would find and even purchase the best deals online without having to go to many different websites and product pages. Yet tech platforms have used lawsuits and data control to stifle such tools. The lack of digital helpers has potentially profound consequences. Social media use is linked to alarming increases in teenage depression and anxiety. Businesses’ ability to manipulate consumers into paying higher …

Book Review: Biglaw's Race Problem, Angela Onwuachi-Willig, Anthony V. Alfieri

Faculty Scholarship

Ever since the 1970s when BigLaw firms began to hire Black lawyers into their associate ranks, these firms have wrestled with problems in both recruiting and retaining Black associates. During the ensuing decades, BigLaw firms have minimally increased the low numbers of Black attorneys who have become partners, particularly equity partners, within their organizations. Numerous scholars have explored how racial bias and discrimination, both within BigLaw firms and greater society, have contributed to such failures in the recruitment, retention, and promotion of Black lawyers. In his new book The Black Ceiling: How Race Still Matters in the Elite Workplace, …

Normalizing Facial Recognition Technology And The End Of Obscurity, Woodrow Hartzog, Evan Selinger, Judy Hyojoo Rhee

Faculty Scholarship

This article argues that facial recognition technology is the most dangerous surveillance tool ever invented. Given the unique threats this morally suspect tool poses to privacy, civil liberties, human flourishing, and democracy, the only appropriate response is a ban. To justify our position, we explain why facial recognition is distinctive among biometrics, clarify how even seemingly benign and positive uses of the technology can trigger dangerous normalization dynamics, and pinpoint why current United States laws (with reverberations in the EU’s AI Act) are designed to accelerate a slippery slope that makes mass surveillance nearly inevitable. Our most fundamental contribution lies …

Copyright Law And Property Law, Keith N. Hylton

Faculty Scholarship

Property is at the core of state law since it is the exclusive power of the individual state governments to define and protect property rights within their jurisdiction. In this paper I will discuss the general connection between copyright and property generally. I will argue that property law sheds important light on copyright law and can help us cut through modern controversies in copyright law. If I am correct in this view, any judge sufficiently familiar with property law doctrines could do better than the Supreme Court of the United States in resolving a new copyright controversy. Specifically, property law …

Ten Tips For Legal Empiricists, Jordana Goodman, Christa J. Laser, Mark A. Lemley, Lisa Larrimore Ouellette, Melissa F. Wasserman, Saurabh Vishnubhakat, Janet Freilich, Jessica Silbey, David L. Schwartz, Neel U. Sukhatme

Faculty Scholarship

This essay offers practical tips from leading legal empirical researchers to assist new scholars who are entering the field. Topics include developing meaningful research questions, designing robust survey methodologies, balancing qualitative and quantitative approaches, ensuring data transparency, fostering interdisciplinary collaboration, and navigating ethical considerations in empirical work. Drawing from their own unique challenges and successes, scholars Michael D. Frakes, Janet Freilich, Mark A. Lemley, Lisa Larrimore Ouellette, David L. Schwartz, Jessica Silbey, Neel U. Sukhatme, Saurabh Vishnubhakat, Melissa F. Wasserman, Jordana Goodman, and Christa Laser illuminate what they wished they would have known going into empirical research and the pitfalls …

Amicus In Wilcox V. Trump On Presidential Removal And Unitary Executive Theorists’ Errors, Jed Handelsman Shugerman

Faculty Scholarship

As courts ask whether Free Enterprise and Seila Law call into question Humphrey’s Executor and independent officers or agencies, the more immediate questions should be focused on new historical evidence and new fact-checking finding pervasive and repeated historical errors and misinterpretations at the foundation of the unitary executive theory of removal.

Since the Supreme Court decided Seila Law, a wave of new historical research has shown that the Founding generation did not understand Article II to grant the President an indefeasible removal power over executive officials. In response, pro-unitary executive scholars have tried to offer new historical support. That …