Physical Sciences and Mathematics Commons^™

Pentanary And Hexanary Alkali Lanthanide Germanates Grown From Lanthanide Rich Flux Growth Reactions, Gregory Morrison, K. Pilar Zamorano, Virginia G. Jones, Ethan N. Adams, Hans Conrad Zur Loye

Faculty Publications

Single crystals of five compounds of a new structure type, the K3Nd52Ge12O100F7 type, were grown from lanthanide rich reactions using a KF/KCl flux: K3Ln52Ge12O100F7 (Ln = Nd, Eu), K3Ln52Ge12O100+δF7-δ (Ln = Pr, Tb), and K3Ca0.872Nd51.128Ge12O99.128F7.872. The five compounds crystallize in the cubic space group I-43d with lattice parameter 22.0350(2) Å for K3Nd52Ge12O100F7. The structure consists of a 3D checkerboard of LnX8, LnX7, LnX6, and GeO4 polyhedra, where X = O/F, and can be conceptually derived from the fluorite structure. K3Eu52Ge12O100F7 exhibits the fluorescence typical of Eu³⁺.

Smarter Disease Detection From Electronic Health Record Data: An End-To-End Ai-Augmented Pipeline For Computable Phenotyping, Dylan Owens

Statistical Science Theses and Dissertations

Electronic Health Records (EHR) contain a wealth of structured and unstructured patient data that can be leveraged for computable phenotyping, the process of algorithmically identifying patient cohorts with specific diseases or conditions. Traditional rule-based phenotyping approaches, while interpretable, often struggle with scalability, portability across institutions, and effective use of unstructured clinical narratives. Recent advances in large language models (LLMs) present new opportunities for synthesizing complex free-text information into concise, clinically meaningful representations. However, integrating LLMs into phenotyping workflows requires careful design to maintain transparency, interpretability, and measurable uncertainty—features essential for clinical adoption and downstream applications such as decision support.

We …

The Pastor As Romantic Author: Ai, Preaching, And The Unacknowledged Inheritance Of Authenticity, Daniel Plate, James Hutson

Faculty Scholarship

This article interrogates contemporary reactions to sermons produced with generative technologies through a historical–conceptual lens, arguing that widespread judgments of such outputs as “soulless,” “generic,” or lacking a “beating heart” are best explained by an unacknowledged inheritance from nineteenth-century Romantic expressivism. Rather than treating resistance to machine authorship as a theological verdict on computational incapacity, the study reconstructs how Romanticism centered authorship in sincere self-expression and solitary genius, displacing earlier heraldic expectations that prized fidelity to a received message. Methodologically, the analysis combines intellectual history with discourse analysis of global Christian experiments in synthetic composition (2020–2025), denominational guidance, and media …

Csc36000 - Modern Distributed Computing Assignment, Saptarashmi Bandyopadhyay

Open Educational Resources

This assignment covers standard performance metrics for Distributed Systems and the basics of Multiprocessing for CSC36000 - Modern Distributed Computing at the City College of New York CUNY. It is an interactive coding assignment intended to be executed in a Python notebook.

Water Current, Volume 57, No. 3, Fall 2025

Water Current Newsletter

No abstract provided.

Delving Into Invisible Semantics For Generalized One-Shot Neural Human Rendering, Yihong Lin, Xuemiao Xu, Huaidong Zhang, Cheng Xu, Weijie Li, Yi Xie, Jing Qin, Shengfeng He

Research Collection School Of Computing and Information Systems

Traditional human neural radiance fields often overlook crucial body semantics, resulting in ambiguous reconstructions, particularly in occluded regions. To address this problem, we propose the Super-Semantic Disentangled Neural Renderer (SSD-NeRF), which employs rich regional semantic priors to enhance human rendering accuracy. This approach initiates with a Visible-Invisible Semantic Propagation module, ensuring coherent semantic assignment to occluded parts based on visible body segments. Furthermore, a Region-Wise Texture Propagation module independently extends textures from visible to occluded areas within semantic regions, thereby avoiding irrelevant texture mixtures and preserving semantic consistency. Additionally, a view-aware curricular learning approach is integrated to bolster the model's …

Stroke2sketch: Harnessing Stroke Attributes For Training-Free Sketch Generation, Rui Yang, Huining Li, Yiyi Long, Xiaojun Wu, Shengfeng He

Research Collection School Of Computing and Information Systems

Generating sketches guided by reference styles requires precise transfer of stroke attributes, such as line thickness, deformation, and texture sparsity, while preserving semantic structure and content fidelity. To this end, we propose Stroke2Sketch, a novel training-free framework that introduces cross-image stroke attention, a mechanism embedded within self-attention layers to establish fine-grained semantic correspondences and enable accurate stroke attribute transfer. This allows our method to adaptively integrate reference stroke characteristics into content images while maintaining structural integrity. Additionally, we develop adaptive contrast enhancement and semanticfocused attention to reinforce content preservation and foreground emphasis. Stroke2Sketch effectively synthesizes stylistically faithful sketches that closely …

Cross-Subject Mind Decoding From Inaccurate Representations, Yangyang Xu, Bangzhen Liu, Wenqi Shao, Yong Du, Shengfeng He, Tingting Zhu

Research Collection School Of Computing and Information Systems

Decoding stimulus images from fMRI signals has advanced with pre-trained generative models. However, existing methods struggle with cross-subject mappings due to cognitive variability and subject-specific differences. This challenge arises from sequential errors, where unidirectional mappings generate partially inaccurate representations that, when fed into diffusion models, accumulate errors and degrade reconstruction fidelity. To address this, we propose the Bidirectional Autoencoder Intertwining framework for accurate decoded representation prediction. Our approach unifies multiple subjects through a Subject Bias Modulation Module while leveraging bidirectional mapping to better capture data distributions for precise representation prediction. To further enhance fidelity when decoding representations into stimulus images, …

Classical Shadows With Improved Median-Of-Means Estimation, Winston Fu, Dax Enshan Koh, Siong Thye Goh, Jian Feng Kong

Research Collection School Of Computing and Information Systems

The classical shadows protocol, introduced by Huang et al (2020 Nat. Phys. 16 1050), makes use of the median-of-means (MoM) estimator to efficiently estimate the expectation values of M observables with failure probability δ using only O ( log ⁡ ( M / δ ) ) measurements. In their analysis, Huang et al used loose constants in their asymptotic performance bounds for simplicity. However, the specific values of these constants can significantly affect the number of shots used in practical implementations. To address this, we studied a modified MoM estimator proposed by Minsker (2023 Proc. 36th Conf. on Learning Theory …

Deep Learning For Hate Speech Detection: A Comparative Study, Jitendra Singh Malik, Hezhe Qiao, Guansong Pang, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

Automated hate speech detection is an important tool in combating the spread of hate speech, particularly in social media. Numerous methods have been developed for the task, including a recent proliferation of deep-learning based approaches. A variety of datasets have also been developed, exemplifying various manifestations of the hate-speech detection problem. We present here a largescale empirical comparison of deep and shallow hate-speech detection methods, mediated through the three most commonly used datasets. Our goal is to illuminate progress in the area, and identify strengths and weaknesses in the current state-of-the-art. We particularly focus our analysis on measures of practical …

Teaching Diffusion Models To Ground Alpha Matte, Tianyi Xiang, Weiying Zheng, Yutao Jiang, Tingrui Shen, Hewei Yu, Yangyang Xu, Shengfeng He

Research Collection School Of Computing and Information Systems

The power of visual language models is showcased in visual understanding tasks, where language-guided models achieve impressive flexibility and precision. In this paper, we ex tend this capability to the challenging domain of image matting by framing it as a soft grounding problem, enabling a single diffusion model to handle diverse objects, textures, and transparencies, all directed by descriptive text prompts. Our method teaches the diffusion model to ground alpha mattes by guiding it through a process of instance-level localization and transparency estimation. First, we introduce an intermediate objective that trains the model to accurately localize semantic components of the …

Stable Score Distillation, Haiming Zhu, Yangyang Xu, Chenshu Xu, Tingrui Shen, Wenxi Liu, Yong Du, Jun Yu, Shengfeng He

Research Collection School Of Computing and Information Systems

Text-guided image and 3D editing have advanced with diffusion-based models, yet methods like Delta Denoising Score often struggle with stability, spatial control, and editing strength. These limitations stem from reliance on complex auxiliary structures, which introduce conflicting optimization signals and restrict precise, localized edits. We introduce Stable Score Distillation (SSD), a streamlined framework that enhances stability and alignment in the editing process by anchoring a single classifier to the source prompt. Specifically, SSD utilizes Classifier-Free Guidance (CFG) equation to achieve cross-prompt alignment, and introduces a constant term null-text branch to stabilize the optimization process. This approach preserves the original content's …

Tactile Data Comics: Combining Step-By-Step Presentation Of Tactile Graphics With Verbal Narration For The Blind And Visually Impaired, Yang Jiao, Ruoting Sun, Rong Luo, Xiwen Yao, Xinran She, Kotaro Hara, Yuewen Zhang, Xinyi Fu

Research Collection School Of Computing and Information Systems

Tactile graphics on a refreshable display have proven effective in enabling visually impaired people to comprehend pictorial content. To further evaluate the effectiveness of refreshable tactile displays in blind education, we designed tactile data comics, a method that combines step-by-step presentation of tactile graphics with verbal narration. We conducted a user study with sixteen visually impaired students to compare tactile data comics against verbal-only and static tactile graphics. Our findings show that tactile data comics significantly improve participants’ comprehension and engagement during the learning experience. These empirical results suggest that the integration of refreshable tactile displays and tactile data comics …

Impact Of Original Versus Reposted Social Endorsements On Content Consumption: The Moderating Role Of Endorsers’ Network Characteristics, Anqi Zhao, Qian Tang

Research Collection School Of Computing and Information Systems

Social endorsements broadcast endorsers’ positive attitudes toward content or products, especially to their social ties. Original endorsements created by endorsers can be propagated further as reposted endorsements. Both are important marketing tools to increase content consumption, yet their differences are unclear. This study compares the impacts of original and reposted endorsements on content consumption and their contingencies on the endorsers’ network characteristics. Using data on social endorsements of YouTube videos on Twitter, we find that original endorsements (i.e., original tweets) significantly boost content consumption, and the effect is positively moderated by the endorsers’ network size but not their tie strength. …

Learn To Fly: Enabling Deep Learning Based Perception And Control In Aerial Robotics, Krishna Muvva

School of Computing: Dissertations, Theses, and Student Research

Uncrewed Aerial Vehicles (UAVs) are increasingly deployed in dynamic, GPS degraded, and cluttered environments, yet their autonomy remains fundamentally constrained by limitations in onboard perception and real-time control. This dissertation addresses these challenges by proposing a unified framework that co-designs deep learning-based perception and model-based control, organized around three core thrusts: Learn to Track, Learn to Localize, and Learn to Evade.

Learn to Track develops dynamic and adaptive perception control mechanisms that optimize CNN inference for target tracking. A control-aware CNN framework dynamically adjusts inference frequency based on UAV motion, reducing latency while maintaining visual lock. An adaptive CNN with …

Integrating Satellite Observations And Modeling Approaches To Study Tectonic And Land Surface Hazards, Jeng Hann Chong

Earth and Planetary Sciences ETDs

The increasing availability of Earth observation satellites, such as SAR and optical platforms, has improved our ability to study the ground deformation processes during, after, and leading up to these natural hazards, such as earthquakes and debris flows. In this dissertation, I analyze satellite geodetic interseismic velocities across the Rakhine-Bangladesh megathrust and the influence of having realistic modeling assumptions for subduction zones to better constrain seismic hazard. I also examined changes in post-fire debris flow likelihood four years after the 2020 Lake Fire in Southern California using a suite of satellite data and field observations. Together, these studies highlight how …

Chemical And Sensory Attributes Of Different Ethanol Reduction Methods In Muscadine Wine Production, Alexandra A. Escalera, Patricia C. Patricio Morillo, Drew Budner, Katherine A. Thompson-Witrick, Andrew J. Macintosh

Chemistry

There has been a recent shift in the global wine market towards reduced-alcohol wines. Muscadine grapes (Vitis rotundifolia) have become a popular choice in many emerging markets; however, their suitability in reduced-alcohol wine production has not been extensively tested. In this study, methods to reduce ethanol in muscadine wine were compared to determine differences in chemical and sensory attributes and consumer preference. The methods evaluated included full fermentation time with Saccharomyces cerevisiae (control), reduced fermentation time with Saccharomyces cerevisiae (stopped fermentation), fermentation with Saccharomycodes ludwigii yeast (instead of Saccharomyces cerevisiae), and vacuum distillation. The control and distilled wines were fermented …

Assessing The Role Of Ocean Literacy In Liberia's Blue Economy : Pathway To Sustainable Blue Ocean Development In Liberia, Precious Tete Nah

Ocean Sustainability, Governance & Management Dissertations

Ocean literacy involves understanding the fundamental concepts of the ocean's functioning, leading to informed and responsible decisions about the ocean. This understanding is vital for developing responsible ocean citizenship and implementing measures to sustain the ocean, as it highlights the ocean's influence on humans and the impact of human activities on it. Using a qualitative research method with a purposive sampling approach, the research asks focus group representatives from New Kru Town and Westpoint, both closed- and open-ended questions, on the importance of ocean literacy, challenges, prospects, and recommendations to promote sustainable blue ocean development.

The results of the research …

Diffusion Of Innovation Of Port Decarbonization Measures : A Case Study Approach, Clint Rocky Mcculloch, Lucas Fernando Vaquero Roviriego

Port Management Dissertations

The dissertation is a study of how stakeholders diffuse innovation in port decarbonization strategies through a case study approach, with emphasis on the Diffusion of Innovation (DOI) relating to the technical and operational measures in the developing regions. Port, being a critical hub in global supply chains, have to manage the increasing pressure of reducing greenhouse gas (GHG) emissions in alignment with International Maritime Organization (IMO) 2023 GHG strategy as well as the Paris Agreement. However, there is a gap in empirical research, specifically in non-European regions like South America and the Caribbean, highlighting the need for insights from stakeholders’ …

Analytical Solutions For Transitional Flow Around Hydrometeors: Implications For Particle Dynamics And Atmospheric Modeling, Ahmad Talaei, Timothy J. Garrett

Physics Student Research

Accurate modeling of hydrometeor dynamics is critical for advancing weather and climate predictions. However, this remains a long-standing challenge in atmospheric science, particularly within the transitional Reynolds number regime, due to the lack of analytical solutions to the Navier–Stokes equations. This study presents two analytical solutions for two-dimensional, incompressible, and viscous flow around a sphere, addressing the gap between low and high Reynolds number regimes. The first framework, an extension of classical expansion methods using hypergeometric, provides a direct analytical relation that characterizes separation angle across the transitional regime. To address post- separation dynamics, a second formulation based on a …

Spatiotemporal Modeling Of Maternal Mortality In South Carolina 2018-2023, Leah Wood, Ray Bai, Emily Mann

Senior Theses

Maternal death serves as a public health indicator due to fact that it is considered preventable with the availability of modern biomedicine, however, it persists broadly throughout the United States. Current literature outlines national trends in maternal mortality with complicating, preexisting conditions, and structural upstream factors often cited as being the largest contributors to increased risk. This study utilizes publicly available, county-level data for maternal death in addition to demographic and descriptive data in order to estimate maternal mortality rates in each of South Carolina’s 46 counties from 2018 to 2023. In order to address sparsity in the outcome variable …

A Better Great Plains: Bridging The Implementation Gap For A Better Future, David W. Sandahl

School of Natural Resources: Dissertations, Theses, and Student Research

The Northern Great Plains is experiencing extensive native grassland loss and landscape degradation, jeopardizing both ecological integrity and the socio-economic systems that rely upon these ecosystems. In an early effort to lessen degradation, the United States Department of Agriculture (USDA) introduced the Conservation Reserve Program (CRP) which generated ecosystem services benefits such as reduced soil erosion, improved water quality, and increased species diversity through the retirement of cropland and rangeland for 10- or 15-year contracts. However, CRP has not fully modernized alongside today’s agriculturalists and adoption remains shaped by an historically fraught socio-political context characterized by deep-seated mistrust between ranchers, …

Active Measurement Of A Micron-Order Gap Under High-Speed And High-Temperature Conditions, Andrew Becker

Doctoral Dissertations and Master's Theses

The hypersonic regime poses numerous challenges that researchers face in the development of hypersonic flight vehicles. Due to their excellent thermomechanical properties, ultra-high-temperature ceramics (UHTCs) have risen as a promising solution to act as a protective barrier between the harsh environment and surface materials of these flight bodies. The mechanical operation of a portable hypersonic simulation device was developed in-house and tested at Argonne National Laboratories (ANL) to gather in-situ material response of prospective UHTC samples when exposed to a hypersonic regime. An edge detection-based algorithm was developed and used in LabVIEW to monitor the health and operation of the …

(Si15-010) On Perturbations Of Gabor Frames, Jamkhongam Touthang

Applications and Applied Mathematics: An International Journal (AAM)

Stability plays a crucial role in frame theory and its applications. The present paper studies the interaction between Gabor frames and perturbations, presenting perturbation results related to small changes of the frame parameters and the window functions both in regular and irregular settings. Examples are provided for illustration. Additionally, the paper briefly discusses algorithms pertinent to Gabor frames under perturbations and highlights challenging areas in the field.

Assessing Expected Cover Of Invasive Flora Species Near Corridor Features By Distance, Joshua G. Logan

Theses

Fragmentation of landscapes has been a growing topic of interest within scientific literature over the past several decades, as the way we as humans affect our planet becomes more apparent as the years go on. As humans, we have shaped the land and what it contains in immeasurable ways, erasing or forever altering entire natural ecosystems. Chief among the drivers altering these ecosystems is the fragmentation of landscapes, which can have all kinds of effects on natural ecosystems, from limiting nutrient transport to providing easier access for invasives to permeate throughout a landscape. Corridors are significant areas of interest regarding …

The Complexity Of Long-Distance Dependencies And Their Impact On Language Models, Abhijit Shrikant Mahalunkar

Doctoral

Sequential data modeling is an important challenge in various fields and in particular in natural language processing. Building effective sequential models faces a notable challenge in the form of Long-Distance Dependencies (LDDs) within the sequence data. Hence, successfully overcoming this challenge is imperative for developing robust and accurate sequential models across various domains and applications. To tackle this challenge, the first step is to conduct a detailed analysis of the complexity of LDDs observed in various sequence datasets. This thesis offers a thorough exploration and documentation of this analysis. An important finding from this thesis is the consistent patterns of …

Analysis Of The Status And Thematic Trends Of Ai For Science Research Abroad From 2015 To 2024, Fangyuan Wang, Huiting Xu, Jinghua Xue

Journal of Scientific Information Research

[Purpose/significance] This paper analyzes the relevant literature in the field of AI for Science（AI4S）in the WoS core database from 2015 to 2024, and sorts out the research status and development trends in this field, aiming to provide forward-looking insights for the application of AI technology in scientific research.

[Method/process] This paper combines bibliometric analysis with the BERTopic model to analyze the publication trends, publishing countries, core authors, and topic identification and development trends in the field of AI4S.

[Result/conclusion] Through bibliometric analysis, this paper reveals the exponential growth trend of AI4S-related literature, and finds that China ranks first in the …

Dcrda: Deadline-Constrained Function Scheduling In Serverless-Cloud Platform, Priyanka Ashok Birajdar, Divya Meena, Anurag Satpathy, Sourav Kanti Addya

Computer Science Faculty Research & Creative Works

The serverless computing model frees developers from operational and management tasks, allowing them to focus solely on business logic. This paper addresses the computationally challenging function-container-virtual machine (VM) scheduling problem, especially under stringent deadline constraints. We propose a two-stage holistic scheduling framework called DCRDA targeting deadline-constrained function scheduling. In the first stage, the function-to-container scheduling is modeled as a one-to-one matching game and solved using the classical Deferred Acceptance Algorithm (DAA). The second stage addresses the container-to-VM assignment, modeled as a many-to-one matching problem, and solved using a variant of the DAA, the Revised-Deferred Acceptance Algorithm (RDA), to account for …

(Si15-084) Application Of Differentiation Matrices Corresponding To Scaling And Wavelet Functions To Integral Equations, Viresh Kumar, Rakesh Kumar

Applications and Applied Mathematics: An International Journal (AAM)

In the present paper, we have studied the technique by which a differentiation matrix corresponding to scaling and wavelet function is helpful in representing an integral equation in discrete form. In this technique, only finite times continuously differentiable scaling and wavelet functions were used for the differentiation matrix. This method explores the applications of compactly supported wavelets in discretization of Fredholm integral equation and provides us with a bridge for the solution of integral equations via compactly supported finitely differentiable wavelets.

(Si15-017) Modeling Construction Project Uncertainty Using Fuzzy Inference And Nature-Inspired Metaheuristic Knowledge-Based Algorithms, M. Kapoor, B. K. Pathak, R. Kumar

Applications and Applied Mathematics: An International Journal (AAM)

Construction projects are influenced by various uncertain factors, including supervisory knowledge, labor expertise, and weather conditions. These uncertain factors have linguistic properties that are challenging to quantify with the help of traditional set theory. To address this, the study applies fuzzy inference systems, specifically the Mamdani fuzzy inference system, to model and evaluate the impact of these uncertainties on construction projects. This fuzzy inference system provides a more nuanced understanding of these factors compared to traditional set theory methods. Experimental results indicate that uncertainties significantly impact construction projects. These findings establish a valuable framework for developing adaptive strategies, enhancing project …