Faculty Publications

Permanent URI for this collection

http://hdl.handle.net/1969.1/93296

The purpose of this Faculty Publications collection is to allow TAMU Faculty to self-deposit products of their research, typically journal articles that can be made openly accessible, but also conference presentations and similar materials.

Faculty, please note that as part of the self-deposit process, the Libraries’ Office of Scholarly Communication would encourage you to apply a Creative Commons (CC) license to your work as you share it here. (For help choosing which type, see: https://creativecommons.org/choose/ .) By default via the submission process link immediately below, the CC license version will be the latest (4.0), with international jurisdiction. (For details, see: https://wiki.creativecommons.org/wiki/License_Versions .) If for any reason you would like to restrict the jurisdiction by country, you will need to contact us at digital@library.tamu.edu before you deposit the item.

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Now showing 1 - 20 of 9091

After Form
(Texas A&M University and NCBDS, 2024-09-20) Tripp, Andrew; Tate, James Michael; He, Weiling
The National Conference on the Beginning Design Student (NCBDS) is the leading organization for peer-reviewed research on beginning design and beginning design education. These proceedings were published in conjunction with the 36th Annual Meeting of the NCBDS, hosted by Texas A&M University in 2021.
Multi-object Data Integration in the Study of Primary Progressive Aphasia
(2024-09-25) Gutierrez , Rene; Scheffler, Aaron; Guhaniyogi, Rajarshi; Gorno-Tempini, Maria; Mandelli, Marilu; Battistella, Giovanni
This article focuses on a multi-modal imaging data application where structural/anatomical information from gray matter (GM) and brain connectivity information in the form of a brain connectome network from functional magnetic resonance imaging (fMRI) are available for a number of subjects with different degrees of primary progressive aphasia (PPA), a neurodegenerative disorder (ND) measured through a speech rate measure on motor speech loss. The clinical/scientific goal in this study becomes the identification of brain regions of interest significantly related to the speech rate measure to gain insight into ND patterns. Viewing the brain connectome network and GM images as objects, we develop an integrated object response regression framework of network and GM images on the speech rate measure. A novel integrated prior formulation is proposed on network and structural image coefficients in order to exploit network information of the brain connectome while leveraging the interconnections among the two objects. The principled Bayesian framework allows the characterization of uncertainty in ascertaining a region being actively related to the speech rate measure. Our framework yields new insights into the relationship of brain regions associated with PPA, offering a deeper understanding of neuro-degenerative patterns of PPA.
Bayesian Data Sketching for Varying Coefficient Regression Models
(2024-09-25) Guhaniyogi, Rajarshi; Baracaldo, Laura; Banerjee, Sudipto
Bayesian scalar-on-tensor regression using the Tucker decomposition for sparse spatial modeling finds promising results analyzing neuroimaging data
(2024-09-25) Spencer, Daniel; Guhaniyogi, Rajarshi; Prado, Raquel; Shinohara, Russell
Modeling with multidimensional arrays, or tensors, often presents a problem due to high dimensionality. In addition, these structures typically exhibit inherent sparsity, requiring the use of regularization methods to properly characterize an association between a tensor covariate and a scalar response. We propose a Bayesian method to efficiently model a scalar response with a tensor covariate using the Tucker tensor decomposition in order to retain the spatial relationship within a tensor coefficient, while reducing the number of parameters varying within the model and applying regularization methods. Simulated data are analyzed to compare the model to recently proposed methods. A neuroimaging analysis using data from the Alzheimer's Data Neuroimaging Initiative shows improved inferential performance compared with other tensor regression methods.
InVA: Integrative Variational Autoencoder for Harmonization of Multi-modal Neuroimaging Data
(2024-09-24) Lei, Bowen; Guhaniyogi, Rajarshi; Chandra, Krishnendu; Scheffler, Aaron; Mallick, Bani
There is a significant interest in exploring non-linear associations among multiple images derived from diverse imaging modalities. While there is a growing literature on image-on-image regression to delineate predictive inference of an image based on multiple images, existing approaches have limitations in efficiently borrowing information between multiple imaging modalities in the prediction of an image. Building on the literature of Variational Auto Encoders (VAEs), this article proposes a novel approach, referred to as Integrative Variational Autoencoder (\texttt{InVA}) method, which borrows information from multiple images obtained from different sources to draw predictive inference of an image. The proposed approach captures complex non-linear association between the outcome image and input images, while allowing rapid computation. Numerical results demonstrate substantial advantages of \texttt{InVA} over VAEs, which typically do not allow borrowing information between input images. The proposed framework offers highly accurate predictive inferences for costly positron emission topography (PET) from multiple measures of cortical structure in human brain scans readily available from magnetic resonance imaging (MRI).
Impact of Library Collections on Faculty Teaching, Research, and Retention: A Mixed-Methods Study
(Association of College & Research Libraries, 2025-11) LeMire, Sarah; Bodenhamer, Shanna
In recent decades, college and university libraries have been called to demonstrate their impact on their institutions’ teaching and research missions. One way that libraries can demonstrate their impact is by evaluating how library collections can influence faculty recruitment and retention decisions. This study builds upon an existing study aimed at evaluating this impact. The authors apply a mixed-methods approach to an existing data set in order to identify differences in impact based upon faculty discipline and rank. The authors found that tenured faculty as well as faculty in the Arts and Humanities were significantly more likely to include the library as part of their recruitment and retention decision making.
Designing Manufacturing Systems Under Energy Scarcity in Expeditionary Environments
(2024-09-12) Patterson, Albert; Vajipeyajula, Bhaskar
Mapping Energy Consumption for Powder Material Extrusion Additive Manufacturing
(2024-09-12) Harmon, George; Kabir, Elnaz; Vajipeyajula, Bhaskar; Patterson, Albert
Designing Additively Manufactured Energetic Materials Based on Property/Process Relationships
(2024-09-12) Afolabi, Samuel; Kabir, Elnaz; Vajipeyajula, Bhaskar; Patterson, Albert
Enhancing Education Through Thoughtful Integration of Large Language Models in Assigned Work
(ASEE - GSW, 2024-03-10) Haikal, Tonia; Lightfoot, Robert Jr
In a world where technology is evolving rapidly, it is essential to note its significant intrusion into the field of education. Technology has made vast amounts of information accessible to students, making them over-reliant on technology and less reliant on nurturing their knowledge and imagination. While limiting technology's usage is impossible to stop, learning how to incorporate it efficiently in the educational system is essential. Integrating machine learning (ML) and artificial intelligence (AI) in education is a significant shift in educational methodologies. This transformation offers the possibility to change learning approaches while presenting challenges in the ethical field. This research paper explores the impact of machine learning (ML) and artificial intelligence (AI), particularly large language models like Chat GPT, on education in our classrooms. This topic is essential because it signifies a change in the methods that educators and students use to engage in a course, transforming the learning outcomes while upholding ethical principles. The application of ML and AI in education has attracted increasing attention, but the long-term effects of these technologies on learning achievements require further investigation. Therefore, we aim to find an approach that allows the integration of ML and AI, specifically Chat GPT, while maintaining high expectations in our classrooms. While tools like Chat GPT hold transforming educational potentials, their integration must be navigated thoughtfully, balancing technological advancements with concept learning and acquisition. In this paper, we utilize quantitative analysis of educational outcomes and observational research to understand the impact of LLM on Education. We will observe firsthand how these technologies are integrated into the classroom and how they affect teaching and learning dynamics.
Formation of Nocturnal Offshore Rainfall near the West Coast of Sumatra: Land Breeze or Gravity Wave?
(Monthly Weather Review, 2021-03) Bai, Hedanqiu; Deranadyan, Gumilang; Schumacher, Courtney; Funk, Aaron; Epifanio, Craig; Ali, Abdullah; Endarwin; Radjab, Fachri; Adriyanto, Riris; Nurhayati, Noer; Nugraha, Yudha; Fauziah, Annisa
fternoon deep convection over the Maritime Continent islands propagates offshore in the evening to early morning hours, leading to a nocturnal rainfall maximum over the nearby ocean. This work investigates the formation of the seaward precipitation migration off western Sumatra and its intraseasonal and seasonal characteristics using BMKG C-band radar observations from Padang and ERA5 reanalysis. A total of 117 nocturnal offshore rainfall events were identified in 2018, with an average propagation speed of 4.5 m s21 within 180 km of Sumatra. Most offshore propagation events occur when the Madden–Julian oscillation (MJO) is either weak (real-time multivariate MJO index , 1) or active over the Indian Ocean (phases 1–3), whereas very few occur when the MJO is active over the Maritime Continent and western Pacific Ocean (phases 4–6). The occurrence of offshore rainfall events also varies on the basis of the seasonal evolution of the large-scale circulation associated with the Asian–Australian monsoons, with fewer events during the monsoon seasons of December– February and June–August and more during the transition seasons of March–May and September–November. Low-level convergence, resulting from the interaction of the land breeze and background low-level westerlies, is found to be the primary driver for producing offshore convective rain propagation from the west coast of Sumatra. Stratiform rain prop- agation speeds are further increased by upper-level easterlies, which explains the faster migration speed of high reflective clouds observed by satellite. However, temperature anomalies associated with daytime convective latent heating over Sumatra indicate that gravity waves may also modulate the offshore environment to be conducive to seaward convection migration.
Preparing Engineering Graduate Students to Engage in Scholarly Communications
(American Society for Engineering Education, 2024-06) Morganti, Dianna; Dunn, Angie
The typical engineering degree plan has several important gaps when reviewed against the research lifecycle. These gaps are often filled in by students learning ad hoc, by overworked faculty over numerous mentoring sessions, or often by the engineering research librarians in workshops and consultations. Purposeful incorporation of a curriculum that fills those gaps, though, can prepare students better for the norms of academia, for the process of research publication, and for critical review of scholarship. Research librarians with both engineering and scholarly communication expertise are uniquely situated to fill in the gaps of the research lifecycle. Scholarly communication skills are vital for high-impact research writing – understanding and critically evaluating scientometrics, reviewing conferences and journals, evaluating and reviewing literature, navigating authorship, planning for data management, understanding various paper types, interpreting disciplinary norms, and more. In 2022, the primary author designed and proposed the semester-long first-year graduate course “Research Lifecycle and Publication in Engineering” to the Multidisciplinary Engineering Department. The first course offering was in Spring of 2023, and the students (and their mentors) had overwhelmingly positive evaluations. Student comments showed that an introduction to scholarly communications at the early graduate research stage was also an introduction to the culture and norms of academia. Many of the students submitted their course papers to conferences or journals, practicing some of the scholarly skills learned in this first-year graduate course. The department made the “Research Lifecycle…” course mandatory for all Interdisciplinary Engineering PhD and Master of Science students, after its first semester. This paper will present the course design for “Research Lifecycle and Publication in Engineering.” It will encourage engineering research librarians, teaching faculty, and curriculum committees in engineering to collaborate to prepare their students to engage in the full research lifecycle
Needs Assessment for Creating partnerships between veterinarians and small and medium-sized ranchers to enhance profitability and sustainability
(Texas A&M University, 2024-08-22) Ritter, Nicola; Gonzales, Molly
Survey Results from surveying veterinarians and producers' perspectives of working with each other to enhance the profitability and sustainability of livestock operations.
Creating partnerships between veterinarians and small and medium-sized ranchers to enhance profitability and sustainability
(Texas A&M University, 2024-08-22) Ritter, Nicola
Survey Questions on Veterinarians' Perspectives of Working with Producers; Survey Questions on Producers' Perspectives of Working with Veterinarians
Creating partnerships between veterinarians and small and medium-sized ranchers to enhance profitability and sustainability
(Texas A&M University, 2024-08-22) Ritter, Nicola
Data set from surveys of veterinarians and producers
Opportunities and challenges: Large language models (LLMs) in engineering design
(2024-08-09) Patterson, Albert
Thrifted Religion: Essays on Finding Religion in Texas Thrift Stores
(2024-08-01) Campbell, Heidi A
This book is a collection of select essays written by TAMU students in the COMM 480: Religious Communication course in Spring 2024. Each student in the class became a research collaborator in the "Thrifting Religion" research project run by Dr Heidi A Campbell. This project documents and studies the different forms of “religious material culture” found through secondhand sales and resale shops. Religious material culture refers to the study of physical objects related to the beliefs and practices of various religions (i.e. prayer beads, religious jewelry, holy books, etc.). Students were asked to select an item from this collection and then write a short report about what that object is, how it is used in religious practices, and what messages about religion the items appeared to communicate. The best essays from the class are featured here, and they tell the story of how religions becomes represented, commodified, and incorporated in unique ways into people's everyday religious life.
Exact QR factorizations of rectangular matrices
(Springer, 2024) Lourenco, Christopher; Moreno-Centeno, Erick
QR factorization is a key tool in mathematics, computer science, operations research, and engineering. This paper presents the roundoff-error-free (REF) QR factorization framework comprising integer-preserving versions of the standard and the thin QR factorizations and associated algorithms to compute them. Specifically, the standard REF QR factorization factors a given matrix $A \in \Z^{m \times n}$ as $A=QDR$, where $Q \in \Z^{m \times m}$ has pairwise orthogonal columns, $D$ is a diagonal matrix, and $R \in \Z^{m \times n}$ is an upper trapezoidal matrix; notably, the entries of $Q$ and $R$ are integral, while the entries of $D$ are reciprocals of integers. In the thin REF QR factorization, $Q \in \Z^{m \times n}$ also has pairwise orthogonal columns, and $R \in \Z^{n \times n}$ is also an upper triangular matrix. In contrast to traditional (i.e., floating-point) QR factorizations, every operation used to compute these factors is integral; thus, REF QR is guaranteed to be an exact orthogonal decomposition. Importantly, the bit-length of every entry in the REF QR factorizations (and within the algorithms to compute them) is bounded polynomially. Notable applications of our REF QR factorizations include finding exact least squares or exact basic solutions (i.e., a rational n-dimensional vector $x$) to any given full column rank or rank deficient linear system $A x = b$, respectively. In addition, our exact factorizations can be used as a subroutine within exact and/or high-precision quadratic programming. Altogether, REF QR provides a framework to obtain exact orthogonal factorizations of any rational matrix (as any rational/decimal matrix can be easily transformed into an integral matrix).
Algorithm 1021: SPEX Left LU, Exactly Solving Sparse Linear Systems via a Sparse Left-looking Integer-preserving LU Factorization
(ACM, 2022) Lourenco, Christopher; Chen, Jinhao; Moreno-Centeno, Erick; Davis, Timothy A.
SPEX Left LU is a software package for exactly solving unsymmetric sparse linear systems. As a component of the sparse exact (SPEX) software package, SPEX Left LU can be applied to any input matrix, A, whose entries are integral, rational, or decimal, and provides a solution to the system, which is either exact or accurate to user-specified precision. SPEX Left LU preorders the matrix A with a user-specified fill-reducing ordering and computes a left-looking LU factorization with the special property that each operation used to compute the L and U matrices is integral. Notable additional applications of this package include benchmarking the stability and accuracy of state-of-the-art linear solvers and determining whether singular-to-double-precision matrices are indeed singular. Computationally, this article evaluates the impact of several novel pivoting schemes in exact arithmetic, benchmarks the exact iterative solvers within Linbox, and benchmarks the accuracy of MATLAB sparse backslash. Most importantly, it is shown that SPEX Left LU outperforms the exact iterative solvers in run time on easy instances and instability as the iterative solver fails on a sizeable subset of the tested (both easy and hard) instances. The SPEX Left LU package is written in ANSI C, comes with a MATLAB interface, and is distributed via GitHub as a component of the SPEX software package and as a component of SuiteSparse.
Exactly Solving Sparse Rational Linear Systems via Roundoff-Error-Free Cholesky Factorizations
(SIAM, 2022) Lourenco, Christopher; Moreno-Centeno, Erick
Exactly solving sparse symmetric positive definite (SPD) linear systems is a key problem in mathematics, engineering, and computer science. This paper derives two new sparse roundoff-error-free (REF) Cholesky factorization algorithms that exactly solve sparse SPD linear systems 𝐴⁢𝑥=𝑏, where 𝐴∈ℚ𝑛⁢𝑥⁢𝑛 and 𝑥,𝑏∈𝑄𝑛⁢𝑥⁢𝑝. The key properties of these factorizations are that (1) they exclusively use integer-arithmetic and (2) in the bit-complexity model, they solve the linear system 𝐴⁢𝑥=𝑏 in time proportional to the cost of the integer-arithmetic operations. Namely, the overhead related to data structures and ancillary operations (those not strictly required to perform the factorization) is subsumed by the cost of the integer arithmetic operations that are essential/intrinsic to the factorization. Notably, to date, our algorithms are the only exact algorithm for solving SPD linear systems with this asymptotically efficient complexity bound. Computationally, we show that the novel factorizations are faster than both sparse rational-arithmetic LDL and sparse exact LU factorization. Altogether, the derived sparse REF Cholesky factorizations present a framework to solve any rational SPD linear system exactly and efficiently.

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