Digital Commons Network^™

Inter-Seasonal Comparison Of Acoustic Propagation In A Thalassia Testudinum Seagrass Meadow In A Shallow Sub-Tropical Lagoon, Kevin M. Lee, Megan S. Ballard, Andrew R. Mcneese, Preston S. Wilson, Gabriel R. Venegas, Mathew C. Zeh, Abdullah Rahman

School of Earth, Environmental, & Marine Sciences Faculty Publications

Acoustic propagation measurements were collected in a seagrass meadow in a shallow lagoon for periods of over 65 h in winter and 93 h in summer. A bottom-deployed sound source transmitted chirps (0.1–100 kHz) every 10 min that were received on a four-receiver horizontal hydrophone array. Oceanographic probes measured various environmental parameters. Daytime broadband acoustic attenuation was 2.4 dB greater in summer than winter, and the median received acoustic energy levels were 8.4 dB lower in summer compared to winter. These differences were attributed in part to seasonal changes in photosynthesis bubble production and above-ground seagrass biomass.

Mechanisms ‘All The Way Down’?, Ioan Muntean

Philosophy Faculty Publications

Book review: Mechanistic Explanations in Physics and Beyond, Brigitte Falkenburg and Gregor Schiemann, editors, European Studies in Philosophy of Science, Vol. 11, Springer International Publishing, Cham 2019, pp.220

Threshold For Learning: The Case For Prioritizing The In-Between Spaces In Education, Xiaodi Zhou

Bilingual and Literacy Studies Faculty Publications

In this paper, the author analyses learning as a function of the threshold between parties and ideas. Using Bakhtinian dialogic theory, literacy learning in particular is envisioned as a dialogue of the threshold. This instructional threshold is viewed both in pedagogical and biological terms. The physiological process of learning is described, making a biological case for instructional scaffolding and the gradual release of responsibility in the classroom. The dialogue of different disciplines serves as integration of several thresholds and further strengthening of learning. Curriculum integration is then promoted as a means to lasting learning.

Instructional Framework For Integrating Cross-Cultural Content Using Culturally Responsive And Linguistically Affirming Pedagogies, J. Joy Esquierdo, Maritza De La Trinidad

Bilingual and Literacy Studies Faculty Publications

This chapter describes an instructional framework for culturally relevant and affirming teaching and curricula that addresses effective ways to interconnect cross-cultural content to expand cultural and content literacy in K-12. The Culturally and Linguistically Affirming Pedagogies for Local Context (CLAP-LC) Framework was developed to create culturally and linguistically affirming content, promote equitable education, and nurture student engagement. The framework centers on the intersection of cultural knowledge and lived experiences of students, families, and communities in content and curriculum to promote student achievement, especially students from minoritized and marginalized groups.

Adaptive Resolution Loss: An Efficient And Effective Loss For Time Series Self-Supervised Learning Framework, Kevin Garcia, Juan Manuel Perez, Yifeng Gao

Computer Science Faculty Publications

Time series data is a crucial form of information that has vast opportunities. With the widespread use of sensor networks, largescale time series data has become ubiquitous. One of the most prominent problems in time series data mining is representation learning. Recently, with the introduction of self-supervised learning frameworks (SSL), numerous amounts of research have focused on designing an effective SSL for time series data. One of the current state-of-the-art SSL frameworks in time series is called TS2Vec. TS2Vec specially designs a hierarchical contrastive learning framework that uses loss-based training, which performs outstandingly against benchmark testing. However, the computational cost …

Adaptive Multiple Distributed Bidirectional Spiral Path Planning For Foraging Robot Swarms, Qi Lu, Ryan Luna

Computer Science Faculty Publications

The Distributed Deterministic Spiral Algorithm (DDSA) has shown great foraging efficiency in robot swarms. However, when the number of robots in the swarm increases, scalability becomes a significant bottleneck due to increased collisions among robots, making it challenging to deploy them in the search space (e.g., 20 robots). To address this issue, we propose an adaptive Multiple-Distributed Bidirectional Spiral Algorithm (MDBSA) that enhances scalability. Our proposed algorithm partitions the squared search arena into multiple identical squared regions and assigns robots to regions dynamically based on the number of regions. In each region, a bidirectional spiral search path is planned, and …

Covert Computation In The Abstract Tile-Assembly Model, Robert M. Alaniz, Timothy Gomez, Andrew Rodriguez, Tim Wylie, David Caballero, Elize Grizzell, Robert Schweller

Computer Science Faculty Publications

There have been many advances in molecular computation that offer benefits such as targeted drug delivery, nanoscale mapping, and improved classification of nanoscale organisms. This power led to recent work exploring privacy in the computation, specifically, covert computation in self-assembling circuits. Here, we prove several important results related to the concept of a hidden computation in the most well-known model of self-assembly, the Abstract Tile-Assembly Model (aTAM). We show that in 2D, surprisingly, the model is capable of covert computation, but only with an exponentialsized assembly. We also show that the model is capable of covert computation with polynomial-sized assemblies …

Pmp: Privacy-Aware Matrix Profile Against Sensitive Pattern Inference For Time Series, Li Zhang, Jiahao Ding, Yifeng Gao, Jessica Lin

Computer Science Faculty Publications

Recent rapid development of sensor technology has allowed massive time series data to be collected and set foundation for the development of data-driven services and applications. During the process, data sharing is often required to allow modelers to perform specific time series data mining tasks based on the need of data owner. The high resolution of time series data brings new challenges in privacy protection, as meaningful information in high-resolution data shifts from concrete point values to shape-based patterns. Numerous research efforts have found that long shape-based patterns could contain more sensitive information and may potentially be extracted and misused …

Multicultural And Social Justice Counseling Within The Scope Of Rehabilitation Counseling, Jeong Han Kim, Kayi Ntinda, Christopher Townsend, Kenneth Thomas, Dong Hun Lee, Cameron Ortega, Hee Jung Yu

School of Rehabilitation Services & Counseling Faculty Publications

Historically, people with disabilities are a marginalized cultural group in that their lived world is less well represented in mainstream services. The rehabilitation counseling profession has the goal to prioritize and empower people with disabilities for thriving in their personal and community lives. However, cultures exist within social systems often characterized by inequities, obliging a need to go beyond multicultural counseling to address social injustices that would occur to populations with historical disadvantages such as people with disabilities. The present study discusses the multicultural counseling movement and its impact in the field of rehabilitation counseling. The study first introduces the …

Innovating With In-Service Mathematics Teachers’ Professional Development: The Intersection Among Mixed-Reality Simulations, Approximation-Of-Practice, And Technology-Acceptance, Jair J. Aguilar, Seokmin Kang

Teaching and Learning Faculty Publications

In-service teachers must be provided with high-quality professional development (PD) opportunities that not only effectively improves and develops their skills, but also engage, and motivate them to continue growing. In this project, we studied in-service teachers’ perceptions, opinions, and mindset toward the use of mixed-reality simulations (MRS) as a potential tool for PD to advance and enhance their pedagogical knowledge for mathematics. We followed a convergent parallel mixed-method approach with 49 in-service teachers who were exposed to a series of simulations sessions. We found evidence that the use and integration of technologies like MRS as a tool for PD is …

Approximations Of Practice And Equity: Surveying The Spaces, Bima Sapkota, Carrie Lee, Liza Bondurant, Heather Howell

School of Mathematical & Statistical Sciences Faculty Publications

After three years of meeting as a working group at PME-NA, we will be editing a book on how several models of approximations of practice support or hinder the creation of equitable mathematics learning and thinking spaces. Participants and working group leaders will serve as critical friends as we use a fishbowl format and small groups to provide spaces for refinement and challenge. Participants will leave with a network of thought partners and a draft chapter. At the end of the working group, we will identify overarching themes about affordances and limitations of several models of digital and non-digital forms …

Borsuk–Ulam Type Theorems For G-Spaces With Applications To Tucker Type Lemmas, Oleg R. Musin, Alexey Yu. Volovikov

School of Mathematical & Statistical Sciences Faculty Publications

In this paper we consider several generalizations of the Borsuk–Ulam theorem for G-spaces and apply these results to Tucker type lemmas for G-simplicial complexes and PL-manifolds.

Probabilistic Solutions Of Fractional Differential And Partial Differential Equations And Their Monte Carlo Simulations, Tamer Oraby, Erwin Suazo, Harrinson Arrubla

School of Mathematical & Statistical Sciences Faculty Publications

The work in this paper is four-fold. Firstly, we introduce an alternative approach to solve fractional ordinary differential equations as an expected value of a random time process. Using the latter, we present an interesting numerical approach based on Monte Carlo integration to simulate solutions of fractional ordinary and partial differential equations. Thirdly, we show that this approach allows us to find the fundamental solutions for fractional partial differential equations (PDEs), in which the fractional derivative in time is in the Caputo sense and the fractional in space one is in the Riesz–Feller sense. Lastly, using Riccati equation, we …

Long-Time Asymptotics Of A Complex Cubic Camassa-Holm Equation, Hongyi Zhang, Yufeng Zhang, Zhijun Qiao

School of Mathematical & Statistical Sciences Faculty Publications

n this paper, we study the Cauchy problem of the following complex cubic Camassa-Holm (ccCH) equation mt=bux+12[m(|u|2−|ux|2)]x−12m(uu¯x−uxu¯),m=u−uxx,

where b is an arbitrary real constant. %By applying ∂¯-steepest descent method, l Long-time asymptotics of the equation is obtained through the ∂¯-steepest descent method. Firstly, based on the spectral analysis of the Lax pair and scattering matrix, the solution of the equation is able to be constructed %can be expressed by %the solution of via solving the corresponding Riemann-Hilbert problem (RHP). Then, we present %obtain different long time asymptotic expansions of the solution u(y,t) in different space-time solitonic regions of ξ=y/t. The …

Quantization Dimension For Inhomogeneous Bi-Lipschitz Ifs, Amit Priyadarshi, Mrinal Kanti Roychowdhury, Manuj Verma

School of Mathematical & Statistical Sciences Faculty Publications

Let ν be a Borel probability measure on a d-dimensional Euclidean space Rd, d≥1, with a compact support, and let (p0,p1,p2,…,pN) be a probability vector with pj>0 for 1≤j≤N. Let {Sj:1≤j≤N} be a set of contractive mappings on Rd. Then, a Borel probability measure μ on Rd such that μ=∑Nj=1pjμ∘S−1j+p0ν is called an inhomogeneous measure, also known as a condensation measure on Rd. For a given r∈(0,+∞), the quantization dimension of order r, if it exists, denoted by Dr(μ), of a Borel probability measure μ on Rd represents the speed at which the nth quantization error of order r …

Quantization For A Set Of Discrete Distributions On The Set Of Natural Numbers, Juan Gomez, Haily Martinez, Mrinal Kanti Roychowdhury, Alexis Salazar, Daniel J. Vallez

School of Mathematical & Statistical Sciences Faculty Publications

The quantization scheme in probability theory deals with finding a best approximation of a given probability distribution by a probability distribution that is supported on finitely many points. In this paper, first we state and prove a theorem, and then give a conjecture. We verify the conjecture by a few examples. Assuming that the conjecture is true, for a set of discrete distributions on the set of natural numbers we have calculated the optimal sets of n-means and the nth quantization errors for all positive integers n. In addition, the quantization dimension is also calculated.

Note On Illuminating Constant Width Bodies, Alexey Glazyrin

School of Mathematical & Statistical Sciences Faculty Publications

Recently, Arman, Bondarenko, and Prymak constructed a constant width body in R n whose illumination number is exponential in n. In this note, we improve their bound by generalizing the construction. In particular, we construct a constant width body in R n whose illumination number is at least (τ + o(1))n, where τ ≈ 1.047.

Constrained Quantization For Probability Distributions, Megha Pandey, Mrinal Kanti Roychowdhury

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, for a Borel probability measure P on a Euclidean space Rk, we extend the definitions of nth unconstrained quantization error, unconstrained quantization dimension, and unconstrained quantization coefficient, which traditionally in the literature known as nth quantization error, quantization dimension, and quantization coefficient, to the definitions of nth constrained quantization error, constrained quantization dimension, and constrained quantization coefficient. The work in this paper extends the theory of quantization and opens a new area of research. In unconstrained quantization, the elements in an optimal set are the conditional expectations in their own Voronoi regions, and it is not true …

Spherical Aluthge Transform, Spherical P And Log-Hyponormality Of Commuting Pairs Of Operators Ii, Hyung Won Kim, Jaewoong Kim, Jasang Yoon

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, we consider the notions of spherical p, log, and w-hyponormality of commuting pairs of operators and study their properties and relations. We first show that spherically p-hyponormal 2-variable weighted shifts of hyponormal operators are invariant under powers and the generalized spherical Aluthge transformation. Second, we study the relation between spherical p-hyponormality and spherical w-hyponormality. Third, we study whether the spherical p-hyponormality with 0<p< 1 is invariant under the spherical Aluthge transformation. Finally, we briefly discuss the relation between log-hyponormality in single operators and spherically log-hyponormality in commuting pairs of operators.

Classification On Boundary-Equilibria And Singular Continuums Of Continuous Piecewise Linear Systems, Hebai Chen, Zhaosheng Feng, Hao Yang, Linfeng Zhou

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, we show that any switching hypersurface of n -dimensional continuous piecewise linear systems is an (n−1) -dimensional hyperplane. For two-dimensional continuous piecewise linear systems, we present local phase portraits and indices near the boundary equilibria (i.e. equilibria at the switching line) and singular continuum (i.e. continuum of nonisolated equilibria) between two parallel switching lines. The index of singular continuum is defined. Then we show that boundary-equilibria and singular continuums can appear with many parallel switching lines.

Equity In Focus : Investigating Gender Disparities In Glioblastoma Via Propensity Score Matching, Solomon Eshun

School of Mathematical & Statistical Sciences Faculty Publications

Gender disparities in health outcomes have garnered significant attention, prompting investigations into their underlying causes. Glioblastoma (GBM), a devastating and highly aggressive form of brain tumor, serves as a case for such inquiries. Despite the mounting evidence on gender disparities in GBM outcomes, investigations specific at the molecular level remain scarce and often limited by confounding biases in observational studies. In this study, I aimed to investigate the gender-related differences in GBM outcomes using propensity score matching (PSM) to control for potential confounding variables. The data used was accessed from the Cancer Genome Atlas (TCGA), encompassing factors such as gender, …

Resurgence Of Habiro Elements, Samuel Crew, Ankush Goswami, Robert Osburn

School of Mathematical & Statistical Sciences Faculty Publications

We prove resurgence properties for the Borel transform of elements in the Habiro ring which satisfy a general type of strange identity. As an application, we provide evidence for (and against) conjectures in quantum topology due to Costin and Garoufalidis.

Tau-Function Formulation For Bright, Dark Soliton And Breather Solutions To The Massive Thirring Model, Junchao Chen, Bao-Feng Feng

School of Mathematical & Statistical Sciences Faculty Publications

In the present paper, we are concerned with the link between the Kadomtsev–Petviashvili–Toda (KP–Toda) hierarchy and the massive Thirring (MT) model. First, we bilinearize the MT model under both the vanishing and nonvanishing boundary conditions. Starting from a set of bilinear equations of two-component KP–Toda hierarchy, we derive multibright solution to the MT model. Then, considering a set of bilinear equations of the single-component KP–Toda hierarchy, multidark soliton and multibreather solutions to the MT model are constructed by imposing constraints on the parameters in two types of tau function, respectively. The dynamics and properties of one- and two-soliton for bright, …

Final Report: Conceptualizing Ethics, Authenticity, And Efficacy Of Simulations In Teacher Education, Carrie Wilkerson Lee, Liza Bondurant, Bima Sapkota, Heather Howell, Yvonne Lai

School of Mathematical & Statistical Sciences Faculty Publications

This working group was a continuation of working groups in 2019 and 2021 that initially aimed to focus on equity in simulations of practice in mathematics teacher education. We began by discussing our conceptualizations of simulations and equity. Next, we reflected on the lack of work that currently exists at the intersection of simulations and equity as well as our limited collective expertise in this space. We proposed the following areas of potential research: Access, Design, Affective Domains, Teaching Practices, Assessment, Critical Conversations. Attendees self-selected into focus groups and met to discuss their current work and how future work could …

Pmp: Privacy-Aware Matrix Profile Against Sensitive Pattern Inference, Li Zhang, Jiahao Ding, Yifeng Gao, Jessica Lin

Computer Science Faculty Publications

Recent rapid development of sensor technology has allowed massive fine-grained time series (TS) data to be collected and set the foundation for the development of data-driven services and applications. During the process, data sharing is often involved to allow the third-party modelers to perform specific time series data mining (TSDM) tasks based on the need of data owner. The high resolution of TS brings new challenges in protecting privacy. While meaningful information in high-resolution TS shifts from concrete point values to local shape-based segments, numerous research have found that long shape-based patterns could contain more sensitive information and may potentially …

Prediction Of Kinase-Substrate Associations Using The Functional Landscape Of Kinases And Phosphorylation Sites, Marzieh Ayati, Serhan Yılmaz, Filipa Blasco Tavares Pereira Lopes, Mark R. Chance, Mehmet Koyutürk

Computer Science Faculty Publications

Protein phosphorylation is a key post-translational modification that plays a central role in many cellular processes. With recent advances in biotechnology, thousands of phosphorylated sites can be identified and quantified in a given sample, enabling proteome-wide screening of cellular signaling. However, for most (> 90%) of the phosphorylation sites that are identified in these experiments, the kinase(s) that target these sites are unknown. To broadly utilize available structural, functional, evolutionary, and contextual information in predicting kinase-substrate associations (KSAs), we develop a network-based machine learning framework. Our framework integrates a multitude of data sources to characterize the landscape of functional relationships …

Exploring Spectral Bias In Time Series Long Sequence Forecasting, Kofi Nketia Ackaah-Gyasi, Sergio Valdez, Yifeng Gao, Li Zhang

Computer Science Faculty Publications

Transformers have achieved great success in the task of time series long sequence forecasting (TLSF) in recent years. However, existing research has pointed out that over-parameterized deep learning models are in favor of low frequency and could be difficult to capture high-frequency information for regression fitting task, named spectral bias. Yet the effect of such bias on TLSF problem, an auto-regressive problem with a long forecasting length, has not been explored. In this work, we take the first step to investigate the spectral bias issues in TLSF task for state-of-the-art models. Specifically, we carefully examine three different existing time series …

The Importance Of The Pressure Anisotropy Induced By Strong Magnetic Fields On Neutron Star Physics, Efrain J. Ferrer, Aric Hackebill

Physics & Astronomy Faculty Publications

In this paper we discuss in some detail how the pressures determined from semi-classical statistical averaging of the energy momentum tensor in the presence of a uniform background magnetic field are anisotropic with different pressures arising along and perpendicular to the magnetic field direction. Hence, we analyze how this result can affect two important characteristics of dense magnetized systems: (i) The hadron-quark phase transition in the presence of a magnetic field, (ii) The behavior of the speed of sound in dense magnetized systems. Taking into account that large magnetic fields are expected to be present in the interior of neutron …

Abmr: An R Package For Agent-Based Model Analysis Of Large-Scale Movements Across Taxa, Benjamin Gochanour, Javier Fernández-López, Andrea Contina

School of Integrative Biological & Chemical Sciences Faculty Publications

1. Agent-based modelling (ABM) shows promise for animal movement studies. However, a robust, open-source and spatially explicit ABM coding platform is currently lacking.
2. We present abmR, an R package for conducting continental-scale ABM simulations across animal taxa. The package features two movement functions, each of which relies on the Ornstein–Uhlenbeck (OU) process.
3. The theoretical background for abmR is discussed and the main functionalities are illustrated using example populations.
4. Potential future additions to this open-source package may include the ability to specify multiple environmental variables or to model interactions between agents. Additionally, updates may offer opportunities for disease ecology and integration with …

Implementation Of Course Structure In Stem Courses For Student Motivation And Learning, And Lab Innovation, Muzammil Arshad, Mamoona Muzammil

School of Integrative Biological & Chemical Sciences (Formerly Dept. of Chemistry)

The present study is an extension of implementation of the course structure which was initially designed, developed and implemented at Texas A&M University for engineering courses. This study extends its implementation to other STEM courses to assess its applicability and effectiveness in science related courses. The course structure is employed at the Chemistry department at University of Texas Rio Grande Valley (UTRGV). The present study is an autoethnography of the implementation of the course structure and its effectiveness assessment. This study highlights the implementation of the course structure considering student motivation and learning since student motivation is an important research …