Maryland Shared Open Access Repository
MD-SOAR is a shared digital repository platform for twelve colleges and universities in Maryland. It is currently funded by the University System of Maryland and Affiliated Institutions (USMAI) Library Consortium (usmai.org) and other participating partner institutions. MD-SOAR is jointly governed by all participating libraries, who have agreed to share policies and practices that are necessary and appropriate for the shared platform. Within this broad framework, each library provides customized repository services and collections that meet local institutional needs. Please follow the links below to learn more about each library's repository services and collections.
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Recent Submissions
Designing Multi-modal Timeline Interfaces to Make Conscious Decisions About Alcohol Intake
(University of Baltimore, 2025-02-11) Martineau, Katherine; Blodgett, Bridget (chair); Donahue, John; The University of Baltimore Yale Gordon College of Arts & Sciences; The University of Baltimore. Master of Science in Interaction Design and Information Architecture
At a time when voice and chat technologies continue to find niches in digital, what tools can be available to people who seek support with reducing alcohol intake? Recent research on smartphone sensing to track alcohol recommends taking a social context approach to address public health hazards of drinking, while many current digital services require self-reporting. Focusing on two audiences, one who wants abstinence, and one that desires to reduce drinking, it is clear that many solutions focus on an abstinence focused approach. Medical practitioners recommend solutions that take a more phased approach to reducing alcohol usage. Traditional programs to reduce drinking in digital technologies focus on cessation and quitting permanently over a phased or reflective approach to reducing alcohol intake. Understanding how communities that experience minority stress, specifically bisexual individuals, can inform ideas of how to create new social spaces that decrease interpersonal stress. What can happen with a timeline, which typically focuses on quantitative metrics, to a solution that focuses more on modulating and thinking through alcohol usage. And, as AI assistants now can get built in code with tools like Claude and ChatGPT. Traditional biometrics like breathalyzers give personalized feedback; how can this evolve into a digital interface that tracks this data over time, and in a private and safe way in a digital interface?
ADAPTIVE IE: Investigating the Complementarity of Human-AI Collaboration to Adaptively Extract Information on-the-fly
(ACL, 2025-01) Mondal, Ishani; Yuan, Michelle; N, Anandhavelu; Garimella, Aparna; Ferraro, Francis; Blair-Stanek, Andrew; Van Durme, Benjamin; Boyd-Graber, Jordan
Information extraction (IE) needs vary over time, where a flexible information extraction (IE) system can be useful. Despite this, existing IE systems are either fully supervised, requiring expensive human annotations, or fully unsupervised, extracting information that often do not cater to user`s needs. To address these issues, we formally introduce the task of “IE on-the-fly”, and address the problem using our proposed Adaptive IE framework that uses human-in-the-loop refinement to adapt to changing user questions. Through human experiments on three diverse datasets, we demonstrate that Adaptive IE is a domain-agnostic, responsive, efficient framework for helping users access useful information while quickly reorganizing information in response to evolving information needs.
Can Generative AI be Egalitarian?
(IEEE, 2024-10) Feldman, Philip; Foulds, James; Pan, Shimei
The recent explosion of “foundation” generative AI models has been built upon the extensive extraction of value from online sources, often without corresponding reciprocation. This pattern mirrors and intensifies the extractive practices of surveillance capitalism [46], while the potential for enormous profit has challenged technology organizations’ commitments to responsible AI practices, raising significant ethical and societal concerns. However, a promising alternative is emerging: the development of models that rely on content willingly and collaboratively provided by users. This article explores this “egalitarian” approach to generative AI, taking inspiration from the successful model of Wikipedia. We explore the potential implications of this approach for the design, development, and constraints of future foundation models. We argue that such an approach is not only ethically sound but may also lead to models that are more responsive to user needs, more diverse in their training data, and ultimately more aligned with societal values. Furthermore, we explore potential challenges and limitations of this approach, including issues of scalability, quality control, and potential biases inherent in volunteercontributed content.
Fair Inference for Discrete Latent Variable Models: An Intersectional Approach
(ACM, 2024-09-04) Islam, Rashidul; Pan, Shimei; Foulds, James
It is now widely acknowledged that machine learning models, trained on data without due care, often exhibit discriminatory behavior. Traditional fairness research has mainly focused on supervised learning tasks, particularly classification. While fairness in unsupervised learning has received some attention, the literature has primarily addressed fair representation learning of continuous embeddings. This paper, however, takes a different approach by investigating fairness in unsupervised learning using graphical models with discrete latent variables. We develop a fair stochastic variational inference method for discrete latent variables. Our approach uses a fairness penalty on the variational distribution that reflects the principles of intersectionality, a comprehensive perspective on fairness from the fields of law, social sciences, and humanities. Intersectional fairness brings the challenge of data sparsity in minibatches, which we address via a stochastic approximation approach. We first show the utility of our method in improving equity and fairness for clustering using naïve Bayes and Gaussian mixture models on benchmark datasets. To demonstrate the generality of our approach and its potential for real-world impact, we then develop a specialized graphical model for criminal justice risk assessments, and use our fairness approach to prevent the inferences from encoding unfair societal biases.
RNA-Puzzles Round V: blind predictions of 23 RNA structures
(Springer Nature, 2024-12-02) Bu, Fan; Adam, Yagoub; Adamiak, Ryszard W.; Antczak, Maciej; de Aquino, Belisa Rebeca H.; Badepally, Nagendar Goud; Batey, Robert T.; Baulin, Eugene F.; Boinski, Pawel; Boniecki, Michal J.; Bujnicki, Janusz M.; Carpenter, Kristy A.; Chacon, Jose; Chen, Shi-Jie; Chiu, Wah; Cordero, Pablo; Das, Naba Krishna; Das, Rhiju; Dawson, Wayne K.; DiMaio, Frank; Ding, Feng; Dock-Bregeon, Anne-Catherine; Dokholyan, Nikolay V.; Dror, Ron O.; Dunin-Horkawicz, Stanisław ; Eismann, Stephan; Ennifar, Eric; Esmaeeli, Reza; Farsani, Masoud Amiri; Ferré-D’Amaré, Adrian R.; Geniesse, Caleb; Ghanim, George E.; Guzman, Horacio V.; Hood, Iris V.; Huang, Lin; Jain, Dharm Skandh; Jaryani, Farhang; Jin, Lei; Joshi, Astha; Karelina, Masha; Kieft, Jeffrey S.; Kladwang, Wipapat; Kmiecik, Sebastian; Koirala, Deepak; Kollmann, Markus; Kretsch, Rachael C.; Kurciński, Mateusz; Li, Jun; Li, Shuang; Magnus, Marcin; Masquida, BenoÎt; Moafinejad, S. Naeim; Mondal, Arup; Mukherjee, Sunandan; Nguyen, Thi Hoang Duong; Nikolaev, Grigory; Nithin, Chandran; Nye, Grace; Pandaranadar Jeyeram, Iswarya P. N.; Perez, Alberto; Pham, Phillip; Piccirilli, Joseph A.; Pilla, Smita Priyadarshini; Pluta, Radosław ; Poblete, Simón; Ponce-Salvatierra, Almudena; Popenda, Mariusz; Popenda, Lukasz; Pucci, Fabrizio; Rangan, Ramya; Ray, Angana; Ren, Aiming; Sarzynska, Joanna; Sha, Congzhou Mike; Stefaniak, Filip; Su, Zhaoming; Suddala, Krishna C.; Szachniuk, Marta; Townshend, Raphael; Trachman, Robert J.; Wang, Jian; Wang, Wenkai; Watkins, Andrew; Wirecki, Tomasz K.; Xiao, Yi; Xiong, Peng; Xiong, Yiduo; Yang, Jianyi; Yesselman, Joseph David; Zhang, Jinwei; Zhang, Yi; Zhang, Zhenzhen; Zhou, Yuanzhe; Zok, Tomasz; Zhang, Dong; Zhang, Sicheng; Żyła, Adriana; Westhof, Eric; Miao, Zhichao
RNA-Puzzles is a collective endeavor dedicated to the advancement and improvement of RNA three-dimensional structure prediction. With agreement from structural biologists, RNA structures are predicted by modeling groups before publication of the experimental structures. We report a large-scale set of predictions by 18 groups for 23 RNA-Puzzles: 4 RNA elements, 2 Aptamers, 4 Viral elements, 5 Ribozymes and 8 Riboswitches. We describe automatic assessment protocols for comparisons between prediction and experiment. Our analyses reveal some critical steps to be overcome to achieve good accuracy in modeling RNA structures: identification of helix-forming pairs and of non-Watson–Crick modules, correct coaxial stacking between helices and avoidance of entanglements. Three of the top four modeling groups in this round also ranked among the top four in the CASP15 contest.