Physical Sciences and Mathematics Commons^™

2024 February - Tennessee Monthly Climate Report, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Monthly Report

No abstract provided.

Cognitive Load Scale In Learning Formal Definition Of Limit: A Rasch Model Approach, Rina Oktaviyanthi, Ria Noviana Agus, Mark Lester B. Garcia, Kornkanok Lertdechapat

Mathematics Faculty Publications

Constructing proofs for the limit using the formal definition induces a high cognitive load. Common assessment tools, like cognitive load scales, lack specificity for the concept of limits. This research aims to validate an instrument tailored to assess cognitive load in students focused on the formal definition of limits, addressing the need for diverse strategies in education. The research employs a quantitative survey design with a Rasch model approach, utilizing a data collection instrument in the form of a questionnaire. Subsequently, the data are analyzed by focusing on three aspects: (1) item fit to the Rasch model, (2) unidimensionality, and …

Reverse Multi-Choice Dialogue Commonsense Inference With Graph-Of-Thought, Li Zheng, Hao Fei, Fei Li, Bobo Li, Lizi Liao, Donghong Ji, Chong Teng

Research Collection School Of Computing and Information Systems

With the proliferation of dialogic data across the Internet, the Dialogue Commonsense Multi-choice Question Answering (DC-MCQ) task has emerged as a response to the challenge of comprehending user queries and intentions. Although prevailing methodologies exhibit effectiveness in addressing single-choice questions, they encounter difficulties in handling multi-choice queries due to the heightened intricacy and informational density. In this paper, inspired by the human cognitive process of progressively excluding options, we propose a three-step Reverse Exclusion Graph-of-Thought (ReX-GoT) framework, including Option Exclusion, Error Analysis, and Combine Information. Specifically, our ReX-GoT mimics human reasoning by gradually excluding irrelevant options and learning the reasons …

Handling Long And Richly Constrained Tasks Through Constrained Hierarchical Reinforcement Learning, Yuxiao Lu, Arunesh Sinha, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Safety in goal directed Reinforcement Learning (RL) settings has typically been handled through constraints over trajectories and have demonstrated good performance in primarily short horizon tasks. In this paper, we are specifically interested in the problem of solving temporally extended decision making problems such as robots cleaning different areas in a house while avoiding slippery and unsafe areas (e.g., stairs) and retaining enough charge to move to a charging dock; in the presence of complex safety constraints. Our key contribution is a (safety) Constrained Search with Hierarchical Reinforcement Learning (CoSHRL) mechanism that combines an upper level constrained search agent (which …

Imitate The Good And Avoid The Bad: An Incremental Approach To Safe Reinforcement Learning, Minh Huy Hoang, Mai Anh Tien, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

A popular framework for enforcing safe actions in Reinforcement Learning (RL) is Constrained RL, where trajectory based constraints on expected cost (or other cost measures) are employed to enforce safety and more importantly these constraints are enforced while maximizing expected reward. Most recent approaches for solving Constrained RL convert the trajectory based cost constraint into a surrogate problem that can be solved using minor modifications to RL methods. A key drawback with such approaches is an over or underestimation of the cost constraint at each state. Therefore, we provide an approach that does not modify the trajectory based cost constraint …

Simple Image-Level Classification Improves Open-Vocabulary Object Detection, Ruohuan Fang, Guansong Pang, Xiao Bai

Research Collection School Of Computing and Information Systems

Open-Vocabulary Object Detection (OVOD) aims to detect novel objects beyond a given set of base categories on which the detection model is trained. Recent OVOD methods focus on adapting the image-level pre-trained vision-language models (VLMs), such as CLIP, to a region-level object detection task via, eg., region-level knowledge distillation, regional prompt learning, or region-text pre-training, to expand the detection vocabulary. These methods have demonstrated remarkable performance in recognizing regional visual concepts, but they are weak in exploiting the VLMs' powerful global scene understanding ability learned from the billion-scale image-level text descriptions. This limits their capability in detecting hard objects of …

Glop: Learning Global Partition And Local Construction For Solving Large-Scale Routing Problems In Real-Time, Haoran Ye, Jiarui Wang, Helan Liang, Zhiguang Cao, Yong Li, Fanzhang Li

Research Collection School Of Computing and Information Systems

The recent end-to-end neural solvers have shown promise for small-scale routing problems but suffered from limited real-time scaling-up performance. This paper proposes GLOP (Global and Local Optimization Policies), a unified hierarchical framework that efficiently scales toward large-scale routing problems. GLOP partitions large routing problems into Travelling Salesman Problems (TSPs) and TSPs into Shortest Hamiltonian Path Problems. For the first time, we hybridize non-autoregressive neural heuristics for coarse-grained problem partitions and autoregressive neural heuristics for fine-grained route constructions, leveraging the scalability of the former and the meticulousness of the latter. Experimental results show that GLOP achieves competitive and state-of-the-art real-time performance …

Mitigating Fine-Grained Hallucination By Fine-Tuning Large Vision-Language Models With Caption Rewrites, Lei Wang, Jiabang He, Shenshen Li, Ning Liu, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have shown remarkable performance in natural language processing (NLP) tasks. To comprehend and execute diverse human instructions over image data, instruction-tuned large vision-language models (LVLMs) have been introduced. However, LVLMs may suffer from different types of object hallucinations. Nevertheless, LVLMs are evaluated for coarse-grained object hallucinations only (i.e., generated objects non-existent in the input image). The fine-grained object attributes and behaviors non-existent in the image may still be generated but not measured by the current evaluation methods. In this paper, we thus focus on reducing fine-grained hallucinations of LVLMs. We propose ReCaption, a framework that consists …

Detecting Outdated Code Element References In Software Repository Documentation, Wen Siang Tan, Markus Wagner, Christoph Treude

Research Collection School Of Computing and Information Systems

Outdated documentation is a pervasive problem in software development, preventing effective use of software, and misleading users and developers alike. We posit that one possible reason why documentation becomes out of sync so easily is that developers are unaware of when their source code modifications render the documentation obsolete. Ensuring that the documentation is always in sync with the source code takes considerable effort, especially for large codebases. To address this situation, we propose an approach that can automatically detect code element references that survive in the documentation after all source code instances have been deleted. In this work, we …

Hackles: Simulating And Visually Representing The Anxiety Of Walking Alone, Sydney Pratte, Anthony Tang, Shannon Hoover, Lora Oehlberg

Research Collection School Of Computing and Information Systems

In this work, we compare the designs of two fashion-tech garments that communicate the anxiety felt when walking alone. While the two garments share a common vision, they are designed to be worn in two radically different settings and to communicate to different audiences: one directly communicates an empathetic experience to its wearer; the other a model wears at a runway show and must share its story to a general audience. We used Research Through Design (RtD) methods to design both fashion-tech garments. Then, we recorded and analyzed the design process for both garments via an annotated portfolio to compare …

Market-Gan: Adding Control To Financial Market Data Generation With Semantic Context, Haochong Xia, Shuo Sun, Xinrun Wang, Bo An

Research Collection School Of Computing and Information Systems

Financial simulators play an important role in enhancing forecasting accuracy, managing risks, and fostering strategic financial decision-making. Despite the development of financial market simulation methodologies, existing frameworks often struggle with adapting to specialized simulation context. We pinpoint the challenges as i) current financial datasets do not contain context labels; ii) current techniques are not designed to generate financial data with context as control, which demands greater precision compared to other modalities; iii) the inherent difficulties in generating context-aligned, high-fidelity data given the non-stationary, noisy nature of financial data. To address these challenges, our contributions are: i) we proposed the Contextual …

From Canteen Food To Daily Meals: Generalizing Food Recognition To More Practical Scenarios, Guoshan Liu, Yang Jiao, Jingjing Chen, Bin Zhu, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

The precise recognition of food categories plays a pivotal role for intelligent health management, attracting significant research attention in recent years. Prominent benchmarks, such as Food-101 and VIREO Food-172, provide abundant food image resources that catalyze the prosperity of research in this field. Nevertheless, these datasets are well-curated from canteen scenarios and thus deviate from food appearances in daily life. This discrepancy poses great challenges in effectively transferring classifiers trained on these canteen datasets to broader daily-life scenarios encountered by humans. Toward this end, we present two new benchmarks, namely DailyFood-172 and DailyFood-16, specifically designed to curate food images from …

Exploring Perceptions Of Algorithmic Bias In Video Interviewing Software: The Importance Of Ai Hiring Education, Theresa Fister, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

Many employers across various fields are incorporating artificial intelligence into their hiring processes. Existing research suggests that although artificial intelligence is often perceived as neutral by its advocates, it can inadvertently perpetuate both implicit and explicit biases present in the engineers and designers responsible for its development through the machine learning process. To delve deeper into this issue, we conducted a research study involving 25 recent graduates from 12 different industries. Our aim was to gain insights into the workings of AI video interviewing software and to understand the reactions of recent graduates who have used this technology. Considering the …

Parameterized Algorithm For The Poset Cover Problem, Ivy D. Ordanel, Proceso L. Fernandez, Richelle Ann B. Juayong, Jhoirene B. Clemente, Henry N. Adorna

Department of Information Systems & Computer Science Faculty Publications

It is already known that the 1-Poset and 2-Poset Cover Problems are in P. In this paper, we extended the previous results and devised an algorithm for the k-Poset Cover Problem, for any k number of posets that cover the input. The algorithm runs in O(m2k n2), where m and n are the input size. With this running time, we can say that the problem belongs to XP (slicewise polynomial). The algorithm runs efficiently for small fixed k but runs exponentially for large k. While the algorithm running time has yet not to be efficient for large k, we have …

From Norms Taker To Norms Breaker: A Comparative Study Of Turkey’S Nuclear Discourses Before And After The Ostensible Coup Of 2016, Sarah Tzinieris, Zenobia S. Homan, Cem Boke, Amna Javed

International Journal of Nuclear Security

This article offers an analysis of public statements made by Turkish government leaders, contrasting official attitudes on nuclear nonproliferation before and after the alleged military coup attempt in 2016. Significant developments in this period include deteriorating democracy and the rule of law in Turkey and the emergence of destabilizing foreign policy differences between Turkey and Western states. President Recep Tayyip Erdoğan also sought to consolidate domestic power and play a more assertive security role in the Middle East. This analysis of official statements reveals a distinct shift in Turkey’s nonproliferation rhetoric after the 2016 coup. In particular, Turkish government ministers …

Improved Subseasonal Prediction Of South Asian Monsoon Rainfall Using Data-Driven Forecasts Of Oscillatory Modes, Eviatar Bach, Venkat Krishnamurthy, Safa Mote, Jagadish Shukla, A. Surjalal Sharma, Eugenia Kalnay, Michael Ghil

Mathematics and Statistics Faculty Publications and Presentations

Predicting the temporal and spatial patterns of South Asian monsoon rainfall within a season is of critical importance due to its impact on agriculture, water availability, and flooding. The monsoon intraseasonal oscillation (MISO) is a robust northwardpropagating mode that determines the active and break phases of the monsoon and much of the regional distribution of rainfall. However, dynamical atmospheric forecast models predict this mode poorly. Data-driven methods for MISO prediction have shown more skill, but only predict the portion of the rainfall corresponding to MISO rather than the full rainfall signal. Here, we combine state-of-the-art ensemble precipitation forecasts from a …

New Algorithmic Support For The Fundamental Theorem Of Algebra, Vitaly Zaderman

Dissertations, Theses, and Capstone Projects

Univariate polynomial root-finding is a venerated subjects of Mathematics and Computational Mathematics studied for four millenia. In 1924 Herman Weyl published a seminal root-finder and called it an algorithmic proof of the Fundamental Theorem of Algebra. Steve Smale in 1981 and Arnold Schonhage in 1982 proposed to classify such algorithmic proofs in terms of their computational complexity. This prompted extensive research in 1980s and 1990s, culminated in a divide-and-conquer polynomial root-finder by Victor Pan at ACM STOC 1995, which used a near optimal number of bit-operations. The algorithm approximates all roots of a polynomial p almost as fast as one …

The Early Late Miocene Cessation Of Deep And Intermediate Water Circulation Through The Central American Seaway, Benjamin Stanley Kwiatkowski

Dissertations, Theses, and Capstone Projects

The state of the middle late Miocene global ocean circulations which regulated climate and affected deep ocean chemical environments were impacted by geographic changes. During the Miocene, cirum-equatorial circulations slowly decreased, which included through the seaway between the Americas called the Central American Seaway (CAS). Its shoaling and ultimate closing led to the onset of modern day oceanic circulation as characterized by the global thermohaline circulation. However, this seaway has a long hotly debated history concerning the timing of its restriction and shallowing owing in part to the shifting of tectonic plates.

The goal of this research focuses on investigating …

Trees In Urban Environments: How Soil Quality Impacts Tree Performance, Saidan Qi

Dissertations, Theses, and Capstone Projects

Cities around the world are increasingly investing in reforestation and afforestation efforts to mitigate impacts from climate change and population growth. However, urban soil conditions can be unfavorable for tree growth. Street trees are widely known to suffer from poor soil quality, but there has been no comprehensive review of this topic so far. Clean soils can be transported from nonurban areas to support cities’ green projects, but this approach is not sustainable. Artificial (constructed) soils can be created from various materials and have been proposed as an alternative medium for urban tree growth, but no research has been done …

Cholesterol Content Regulates The Interaction Of Αa-, Αb-, And Α-Crystallin With The Model Of Human Lens-Lipid Membranes, Raju Timsina, Preston Hazen, Geraline Trossi-Torres, Nawal K. Khadka, Navdeep Kalkat, Laxman Mainali

Physics Faculty Publications and Presentations

α-Crystallin (αABc) is a major protein comprised of αA-crystallin (αAc) and αB-crystallin (αBc) that is found in the human eye lens and works as a molecular chaperone by preventing the aggregation of proteins and providing tolerance to stress. However, with age and cataract formation, the concentration of αABc in the eye lens cytoplasm decreases, with a corresponding increase in the membrane-bound αABc. This study uses the electron paramagnetic resonance (EPR) spin-labeling method to investigate the role of cholesterol (Chol) and Chol bilayer domains (CBDs) in the binding of αAc, αBc, and αABc to the Chol/model of human lens-lipid (Chol/MHLL) membranes. …

Association Of Alpha-Crystallin With Human Cortical And Nuclear Lens Lipid Membrane Increases With The Grade Of Cortical And Nuclear Cataract, Preston Hazen, Geraline Trossi-Torres, Raju Timsina, Nawal K. Khadka, Laxman Mainali

Physics Faculty Publications and Presentations

Eye lens α-crystallin has been shown to become increasingly membrane-bound with age and cataract formation; however, to our knowledge, no studies have investigated the membrane interactions of α-crystallin throughout the development of cataracts in separated cortical membrane (CM) and nuclear membrane (NM) from single human lenses. In this study, four pairs of human lenses from age-matched male and female donors and one pair of male lenses ranging in age from 64 to 73 years old (yo) were obtained to investigate the interactions of α-crystallin with the NM and CM throughout the progression of cortical cataract (CC) and nuclear cataract (NC) …

Machine Learning For Refining Knowledge Graphs: A Survey, Budhitama Subagdja, D. Shanthoshigaa, Zhaoxia Wang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Knowledge graph (KG) refinement refers to the process of filling in missing information, removing redundancies, and resolving inconsistencies in knowledge graphs. With the growing popularity of KG in various domains, many techniques involving machine learning have been applied, but there is no survey dedicated to machine learning-based KG refinement yet. Based on a novel framework following the KG refinement process, this paper presents a survey of machine learning approaches to KG refinement according to the kind of operations in KG refinement, the training datasets, mode of learning, and process multiplicity. Furthermore, the survey aims to provide broad practical insights into …

Mutation Analysis For Evaluating Code Translation, Giovani Guizzo, Jie M. Zhang, Federica Sarro, Christoph Treude, Mark Harman

Research Collection School Of Computing and Information Systems

Source-to-source code translation automatically translates a program from one programming language to another. The existing research on code translation evaluates the effectiveness of their approaches by using either syntactic similarities (e.g., BLEU score), or test execution results. The former does not consider semantics, the latter considers semantics but falls short on the problem of insufficient data and tests. In this paper, we propose MBTA (Mutation-based Code Translation Analysis), a novel application of mutation analysis for code translation assessment. We also introduce MTS (Mutation-based Translation Score), a measure to compute the level of trustworthiness of a translator. If a mutant of …

Recommendations With Minimum Exposure Guarantees: A Post-Processing Framework, Ramon Lopes, Rodrigo Alves, Antoine Ledent, Rodrygo L. T. Santos, Marius Kloft

Research Collection School Of Computing and Information Systems

Relevance-based ranking is a popular ingredient in recommenders, but it frequently struggles to meet fairness criteria because social and cultural norms may favor some item groups over others. For instance, some items might receive lower ratings due to some sort of bias (e.g. gender bias). A fair ranking should balance the exposure of items from advantaged and disadvantaged groups. To this end, we propose a novel post-processing framework to produce fair, exposure-aware recommendations. Our approach is based on an integer linear programming model maximizing the expected utility while satisfying a minimum exposure constraint. The model has fewer variables than previous …

A Finite Element Model For Hydro-Thermal Convective Flow In A Porous Medium: Effects Of Hydraulic Resistivity And Thermal Diffusivity, S. M. Mallikarjunaiah, Dambaru Bhatta

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

In this article, a finite element model is implemented to analyze hydro-thermal convective flow in a porous medium. The mathematical model encompasses Darcy’s law for incompressible fluid behavior, which is coupled with a convection-diffusion-type energy equation to characterize the temperature in the porous medium. The current investigation presents an efficient, stable, and accurate finite element discretization for the hydro-thermal convective flow model. The well-posedness of the proposed discrete Galerkin finite element formulation is guaranteed due to the decoupling property and the linearity of the numerical method. Computational experiments confirm the optimal convergence rates for a manufactured solution. Several numerical results …

Iowa Waste Reduction Center Newsletter, February 2024, University Of Northern Iowa. Iowa Waste Reduction Center.

Iowa Waste Reduction Center Newsletter

In this issue:

--- Conference Season in Full Swing
--- ISOSWO Student Conference Scholarship Deadline
--- Diane Albertson Memorial Scholarship
--- Keeping Tabs on PFAS
--- Coating Workshop Coming to Des Moines
--- Important Reminders

Exciton Dynamics, Interaction, And Transport In Monolayers Of Transition Metal Dichalcogenides, Saroj Chand

Dissertations, Theses, and Capstone Projects

Monolayers Transition metal dichalcogenides (TMDs) have attracted much attention in recent years due to their promising optical and electronic properties for applications in optoelectronic devices. The rich multivalley band structure and sizable spin-orbit coupling in monolayer TMDs result in several optically bright and dark excitonic states with different spin and valley configurations. In the proposed works, we have developed experimental techniques and theoretical models to study the dynamics, interactions, and transport of both dark and bright excitons.

In W-based monolayers of TMDs, the momentum dark exciton cannot typically recombine optically, but they represent the lowest excitonic state of the system …

A Causal Inference Approach For Spike Train Interactions, Zach Saccomano

Dissertations, Theses, and Capstone Projects

Since the 1960s, neuroscientists have worked on the problem of estimating synaptic properties, such as connectivity and strength, from simultaneously recorded spike trains. Recent years have seen renewed interest in the problem coinciding with rapid advances in experimental technologies, including an approximate exponential increase in the number of neurons that can be recorded in parallel and perturbation techniques such as optogenetics that can be used to calibrate and validate causal hypotheses about functional connectivity. This thesis presents a mathematical examination of synaptic inference from two perspectives: (1) using in vivo data and biophysical models, we ask in what cases the …

Modeling Of Covid-19 Clinical Outcomes In Mexico: An Analysis Of Demographic, Clinical, And Chronic Disease Factors, Livia Clarete

Dissertations, Theses, and Capstone Projects

This study explores COVID-19 clinical outcomes in Mexico, focusing on demographic, clinical, and chronic disease variables to develop predictive models. In the binary classification task, the Ada Boost Classifier distinguishes survivors from non-survivors, with age, sex, ethnicity, and chronic medical conditions influencing outcomes. In multiclass classification, the Gradient Boosting Classifier categorizes patients into outcome groups.

Demographic variables, especially age, are crucial for predicting COVID-19 outcomes for both the binary and multiclass classification tasks. Clinical information about previous conditions, including chronic diseases, also holds relevance, especially diabetes, immunocompromise, and cardiovascular diseases. These insights inform public health measures and healthcare strategies, emphasizing …

Towards Sociobiogeochemistry: Critical Perspectives On Anthropogenic Alterations To Soil Nitrogen Chemistry Via U.S. Urban And Suburban Development, Christopher D. Ryan

Dissertations, Theses, and Capstone Projects

The ecological impacts of changes to land use are relevant to concerns about climate change, eutrophication of waterbodies, and reductions in biodiversity. As a foundational component of ecosystem functioning, changes to soil biogeochemistry have significant effects on overall ecosystem health. With cities continuing to grow and develop in extent, the impacts of urbanization and suburbanization on soils are of particular concern. Despite a wide range of natural climatic and geologic conditions, several factors have driven similar patterns of land transformation and management across the United States. In particular, federal initiatives including the Home Owners Loan Corporation, the Federal Housing Administration, …