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Constrained Neural Intelligence: Investigations Into Deep Multi-Objective Optimization, Naveed Tahir

Dissertations - ALL

Neural intelligence is identified with complex learning problems optimized over the high-dimensional, non-convex parameter spaces of deep neural networks. Solving such problems generally requires handling competing objectives and conflicting constraints. This is traditionally dealt with using heuristic methods that collapse such complexity into unconstrained, singular objectives. While computationally convenient, this invites a tradeoff against the precision and stability afforded by non-heuristic, geometry-aware approaches. This dissertation explores constrained multi-objective learning in various applied and theoretical contexts and highlights the feasibility of approximate methods as well as the necessity of exact methods. We propose a framework for equality-constrained deep learning via approximate …

Multi-Modal Survival Prediction On Breast Cancer Mammograms And Enabling Multi-Institutional Collaborations With Federated Learning, Nikolas Koutsoubis

USF Tampa Graduate Theses and Dissertations

Cancer remains one of the leading causes of mortality worldwide, and machine learning is well positioned to leverage the large volumes of routinely collected clinical and imaging data to improve patient outcomes. Realizing this potential at scale requires three capabilities that current practice does not yet deliver in combination: models that integrate the multiple data modalities clinicians use, training procedures that respect institutional data-sharing constraints, and tooling that enables sharing of de-identified data when federated approaches are not sufficient. This dissertation develops methods and infrastructure addressing each of these capabilities in the context of breast cancer and oncology more broadly. …

Journal Acknowledgements And Editorial Board, Academy Editors

Journal of the Arkansas Academy of Science

No abstract provided.

Keynote Address And Meeting Program, Academy Editors

Journal of the Arkansas Academy of Science

No abstract provided.

Business Meeting Report (Secretary's And Treasurer's Report), Academy Editors

Journal of the Arkansas Academy of Science

No abstract provided.

Binucleation And Seasonal Cycle Of Sertoli Cells In The Western Lesser Siren, Siren Nettingi (Caudata: Sirenidae), S. E. Trauth

Journal of the Arkansas Academy of Science

I examined binucleation and the seasonal cycle of Sertoli cells in the Western Siren (Siren nettingi) using light microscopy and transmission electron microscopy. My primary objective in this study was to report, for the first time, the presence of double nuclei in Sertoli cells in a salamander. This study also represents the sixth contribution in a series of investigations that highlight the novel nature of all aspects of spermatogenesis in this unique salamander. My findings add to a growing list of extraordinary morphological and reproductive features that distinguish this salamander from all other vertebrates.

A Scoping Review Of Sycophancy In Large Language Models: Operational And Theoretical Recognition, Kallen Zhou, Manning Littlejohn, Isabella Garrard

Endeavors: Mississippi State Undergraduate Research Journal

As large language models (LLMs) usage grows across different domains, sycophancy, the tendency for output to align with users, is increasingly being recognized as a primary issue arising from applying LLMs into critical areas. Current research has provided a variety of theoretical definitions, mitigation techniques, and quantification for sycophancy. However, there is little to no consistency across different papers. This scoping review seeks to connect different works on LLM sycophancy by identifying themes in theoretical definitions, measurement methods, and inducement techniques of sycophancy. By analyzing 26 papers (preprints, conference proceedings, and journal articles) from arXiv, ACL Anthology, and Scopus, this …

Generalized Delayed Black–Scholes Formula, Bi Gole Hubert Le, Auguste Aman

Journal of Stochastic Analysis

The aim of this paper is to derive an explicit pricing formula for European options when the underlying asset follows a linear generalized delay differential equation in two distinct financial markets. The pricing methodology is based on the construction of an equivalent martingale measure using Girsanov’s theorem. Our models preserve both the no-arbitrage condition and market completeness. As such, this work extends the framework previously developed by Arriojas et al. in [16], providing a broader class of delay-based option pricing models.

Robust Optimal Consumption, Investment And Reinsurance For Recursive Preferences, Elizabeth Dadzie, Wilfried Kuissi Kamdem, Marcel Ndengo

Journal of Stochastic Analysis

This paper investigates a robust optimal consumption, investment, and reinsurance problem for an insurer with Epstein-Zin recursive preferences operating under model uncertainty. The insurer’s surplus follows the diffusion approximation of the Cramér-Lundberg model, and the insurer can purchase proportional reinsurance. Model ambiguity is characterised by a class of equivalent probability measures, and the insurer, being ambiguity-averse, aims to maximise utility under the worst-case scenario. By solving the associated coupled forward-backward stochastic differential equation (FBSDE), we derive closed-form solutions for the optimal strategies and the value function. Our analysis reveals how ambiguity aversion, risk aversion, and the elasticity of intertemporal substitution …

An Itô Integral For A Two-Sided Lévy Process, Raluca Balan, Jaime Garza

Journal of Stochastic Analysis

In this article, we construct an Itô integral with respect to a two-sided finite-variance Lévy process {L(x)}x∈R, without a Gaussian component. Using Rosenthal inequality for discrete-time martingales, we give an estimate for the p-th moment of this integral, for any even integer p ≥ 2. Then, using Poisson-Malliavin calculus, we show that the Itô integral is an extension of the Hitsuda-Skorokhod integral with respect to the compensated Poisson random measure associated to the Lévy process.

Advances In Research On Methods For Intelligent Identification Of Seismic Facies, Liu Xingye, Yu Peilin, He Hengjun

Coal Geology & Exploration

Background The intelligent identification of seismic facies can significantly improve the efficiency of sedimentary system characterization and hydrocarbon reservoir interpretation. However, influenced by factors such as non-stationary geological bodies, high costs of sample labeling, and limited training samples, conventional methods for intelligent identification are generally insufficient to achieve high identification accuracy and widespread application concurrently. Advances This study presents a systematic review of three types of technologies for the intelligent identification of seismic facies, namely unsupervised, supervised, and semi-supervised learning, with each type including deep learning methods. The three technological types are comparatively verified using 3D seismic data from a …

Two-Stage Calibration And Optimization Method For Microscopic Traffic Simulation Model Parameters Based On Neural Network Surrogate Models, Yijia Liu, Chenjing Zhou, Dong Pan, Jian Rong, Yang Xiao

Journal of System Simulation

A two-stage calibration and optimization method is proposed to address the problem that parameter calibration methods for microscopic traffic simulation models are time-consuming. In the first stage, a surrogate model based on neural networks is trained to establish the mapping relationship between model parameters and evaluation indicators, and a genetic algorithm (GA) is combined to screen candidate parameters. In the second stage, after obtaining the approximate optimal parameters, by employing this set of parameters as initial values, a genetic algorithm is re-executed by combining the real simulation model for optimization to further improve calibration accuracy. Experimental results show that the …

Frequency And Radiative Analysis Of Random Yagi-Uhf/Vhf Phased Array, Luis M. Bres, Luis A. Hernandez, Teviet Creighton

Physics & Astronomy Faculty Publications

This study investigates a phased array ground station capable of tracking multiple sources, multi-beamforming, electronic steering, easy scaling, and low cost. The project will develop a 20-pair dual-polarized Yagi-UHF/VHF phased array, comparing the parameters of random and uniform layouts. This study will be divided into two domains: frequency, and radiative domain. The frequency domain will explore the reception and transmission spectra analysis under several scenarios for our antennas. The radiative domain will explore the following: general side lobe analysis across both elevation and azimuth, antenna element sweep analysis, electronic beam steering analysis, electro-mechanical beam steering analysis, array density analysis, and …

Building Trustworthy Information Systems: A Unified Framework For Comparative Risk Detection, Parisa Momeni

USF Tampa Graduate Theses and Dissertations

Risk detection in large scale information systems increasingly depends on heterogeneous data generatedby both centralized and distributed ecosystems. While centralized systems provide curated and validated reports, distributed environments produce large-scale and real-time observational evidence. Existing computational approaches analyze these ecosystems in isolation, limiting systematic comparison of risk repre-sentations across heterogeneous sources.

This dissertation presents a unified computational framework for comparative risk detection across centralized and distributed information systems. The framework provides a domain independent methodology for transforming heterogeneous risk reporting data into comparable multidimensional representations. To enable interpretable comparison of heterogeneous risk distributions, this work introduces the Geometric Overlap Score …

Designing For Fitness And Resilience In Human Artificial Intelligence Systems, Thomas R. Gill

USF Tampa Graduate Theses and Dissertations

Over the last decade, new artificial intelligence models, frameworks, and applications have emerged that have radically changed the socio-technical landscape. These new technologies represent both great opportunities and great potential risks. Due to the recency of their emergence, there is little known about the longevity and long-term repercussions of artifacts powered by new artificial intelligence technology. In this dissertation, I evaluate A.I. and A.I.-based artifacts through the lens of the fitness-utility model, a tool developed by Gill and Hevner (2013) for the evaluation of an artifact's ability to survive and reproduce over long time horizons. In so doing, I also …

The Soil Crisis In Modern Food Systems: Rethinking Agricultural Land Use, Antonia Moure Richard

Journal of Food Law & Policy

Feeding a larger world while preserving the resource that makes agriculture possible—soil—poses a governance problem. By 2050, food systems must support 9.8 billion people even as prevailing practices continue to degrade soils that are non-renewable on human timescales. Technological fixes (e.g., vertical farming, hydroponics) may complement production, but they cannot substitute for soil at scale. The question that follows is simple: are current uses of soil compatible with the future needs of food systems? This article argues that without a shift in governance, short-run productivity gains are achieved by drawing down the soil asset, thereby undermining long-run food security and …

Supersymmetric Quantum Fields Via Quantum Probability, Radhakrishnan Balu

Journal of Stochastic Analysis

The super version of imprimitivity theorem is available now to describe global supersymmetry of systems using the representations of super Lie groups (SLG). This result uses the equivalence between super Harish- Chandra pairs and super Lie groups, at the categorical level, and is applicable to super Poincaré group and generalizes a smooth SI to super context. We apply the result to build supersymmetric quantum fields. Towards this end, we set up a super Fock space of a disjoint union of super Hilbert spaces which is equivalent to super tensoring of boson (even) part symmetrically and that of fermion (odd) part …

Convergence To Fractional Brownian Motion For Weighted Random Sums In Besov Space, Ibrahima Mendy

Journal of Stochastic Analysis

We consider infinite sums of weighted i.i.d. random variables, with finite variance and arbitrary distribution, and we derives conditions for the weak convergence in Besov space of normalized sums to fractional Brownian motion (fBm).

Pricing Variance Swaps Using Extended Heston Model, Semere Gebresilasie, Mulue Gebreslasie, Indranil Sengupta

Journal of Stochastic Analysis

Abstract. In this study, we introduce a variance swap for the underlying asset utilizing the Heston model, incorporating a long-term variance that is treated as a stochastic function of time. We develop a closed-form solution for the variance swap under this framework, where the log returns are driven by a compound Poisson process. Our analysis of historical data reveals that long-term variance is not constant; instead, it fluctuates over time, reflecting market dynamics more accurately. By integrating this time-varying long-term variance into the model, we achieve an improvement in prediction performance of approximately 60%. Furthermore, we perform model calibration using …

Non-Euclidean Geometries And Fairness Constraints In Advanced Clustering, Arnab Seal

Master’s Dissertations

A fundamental challenge in modern unsupervised learning is adapting classical clustering algorithms to handle complex, real-world data constraints. Traditional models often assume data resides in a flat, Euclidean space and optimize strictly for cluster cohesion, thereby failing to capture intrinsic hierarchical structures and ignoring sociotechnical demographic biases. This thesis addresses these critical limitations by extending generalized mean-shift dynamics into two novel clustering frameworks. First, to natively accommodate data with tree-like structures (e.g., taxonomies and social networks), we propose Hyperbolic Gaussian Blurring Mean Shift (HypeGBMS). By projecting data into the Poincar´e ball model and utilizing M¨obius vector space operations, HypeGBMS successfully …

Predictive Importance Sampling Based Coverage Verification For Multi Uav Trajectory Planning, Snehashish Ghosh

Master’s Dissertations

In next-generation wireless networks, unmanned aerial vehicle (UAV) networks are emerging as a promising solution for ultra-reliable low-latency communication (URLLC). A key challenge in millimeter-wave UAV networks is ensuring that mobile users are always in line-of-sight (LoS) coverage, since the current snapshot-based trajectory planning approach does not consider the mobility of the users during the decision interval, resulting in disastrous LoS gaps. For continuous coverage verification, standard uniform sampling is too computationally expensive, as it would need a large number of samples to estimate rare failure events that have latencies that are not suitable for real-time requirements. In this work, …

Photoluminescent Studies Of Metal-Organic Frameworks For Small Molecule Sensing, Julia T. Dematteo

USF Tampa Graduate Theses and Dissertations

Metal–organic frameworks (MOFs) are highly tunable nanoporous materials with broad applications in gas separation, drug delivery, light harvesting, and chemical sensing. The incorporation of photoactive components into MOFs enables the development of optical sensors capable of detecting a wide range of analytes. Porphyrins and metalloporphyrins are particularly attractive for such applications due to their high molar absorptivity, strong emission quantum yields, and versatile interactions with chemical species.

In this work, two porphyrinic MOFs, PCN-222 and MOF1(Cd), are investigated. Both frameworks utilize tetrakis(4-carboxyphenyl)porphyrin (TCPP) as the organic linker, imparting optical functionality to the materials. Integration of TCPP into the framework induces …

Movement Ecology And Population Dynamics Of Box-Nesting Black-Bellied Whistling-Ducks (Dendrocygna Autumnalis) In Louisiana, Katie E. Miranda

LSU Master's Theses

Understanding demographic and behavioral processes underlying population dynamics is essential for effective wildlife management, especially for species subject to harvest pressure or experiencing population shifts. Black-bellied Whistling-Ducks (Dendrocygna autumnalis) are currently undergoing a notable northward range expansion and are increasing in abundance across the southeastern United States, raising management concerns for both ecological and economic reasons. As a result, there is growing public support for a special Black-bellied Whistling-Duck-specific harvest season, yet little research has been conducted on this species, particularly within newly-established populations. To address this gap, I conducted this study with the goal of evaluating seasonal …

Advancing Data Usability, Activity Modeling, And Stability Optimization In Computational Enzyme Design, Spencer Gardiner

Theses and Dissertations

A grand challenge of computational biology is to computationally design, in a single pass, a protein sequence that catalyzes an arbitrary chemical reaction at a high rate under specified conditions [1]. This work details advances in three essential areas on the path to that goal: data quality, activity prediction and modeling, and stability optimization. The structure and implementation of the Allotrope Simple Model (a FAIR data format for many scientific instruments) was examined in [2], setting the stage for training deep learning models on high-quality experimental datasets from diverse sources. In [3], the limits of physics-based and deep learning tools …

Beyond Single Images: A Comprehensive Benchmark For Album-Level Vision-Language Understanding, Shawn Huang

Theses and Dissertations

Automatic album organization has been studied extensively over the past decades due to significant progress in digital photography. Recent Vision-Language Models (VLMs) have shown strong performance on multi-image understanding, making them natural candidates for automating album organization workflows. While VLMs’ abilities in multi-image understanding have been widely studied, their performance on album organization remains underexplored. To bridge this gap, we introduce AlbumBench, the first comprehensive benchmark for automatic album organization. Specifically, we (1) define album organization tasks as photo selection for album-specific user objectives, photo rating according to how well user intents are fulfilled, and album-specific photo grouping given a …

To What Extent Could Quantum Computing Pose A Threat To Global Modern Data Security?, Aniket Maheshwari

Journal of Cybersecurity Education, Research and Practice

Quantum computing has emerged as a transformative technology with the potential to fundamentally disrupt modern cryptographic systems that underpin global data security. This paper examines the extent to which quantum computing could pose a threat to modern global data security by synthesising existing technical, institutional, and policy-oriented literature. Drawing on a narrative review of scholarly research, industry reports, and government frameworks, the analysis focuses on the implications of quantum algorithms such as Shor’s and Grover’s, which challenge the mathematical foundations of widely used cryptographic schemes. The findings suggest that while quantum computing presents a credible long-term threat to asymmetric encryption …

Label-Flip Attack Detection Via Trust-Weighted Aggregation In Federated Learning For Underground Mine Security, Md Sazedur Rahman, Sanjay Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

Underground mining operations are increasingly dependent on autonomous vehicles, robotic drilling systems, and intelligent inspection platforms operating in confined, GPS-denied tunnel environments. These systems rely on distributed perception models to interpret navigation cues, hazard warnings, and environmental signals in real time. While centralized deep learning can enhance model performance, transferring raw operational data across mining sites introduces serious confidentiality and security risks. Federated Learning (FL) offers a privacy-preserving alternative by enabling collaborative model training without sharing local datasets. However, deploying FL in underground mining introduces several critical challenges: (i) Training labels may be modified either maliciously by compromised clients or …

Van Der Waals Interactions Through The Lens Of 2d Materials With Emphasis On Many-Body Effects, Thi Xuan Diem Dang

USF Tampa Graduate Theses and Dissertations

This dissertation addresses the long-standing challenge of accurately and efficiently modeling long-range electron correlations, with a particular focus on van der Waals (vdW) interactions. These interactions play an essential role in predicting the structure, stability, and functionality of molecular systems and condensed matter materials. Although Density Functional Theory (DFT) has become a robust cornerstone of computational materials science, traditional formulations of the theory frequently fail to capture dispersive interactions, leading to significant errors in determining interlayer distances, binding energies, and stacking configurations. These limitations become particularly pronounced in layered materials and vdW heterostructures (HSTs), where weak long-range correlations are sensitive …

Re: Conditional Approval Letter For The Butte Priority Soils Operable Unit (Bpsou) Clark Tailings Consolidated Waste Management Area (Ctcwma) Site Investigation Quality Assurance Project Plan (Qapp) (Dated June 1, 2026), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Where Evidence-Based Medicine Meets Ai: Promise, Pitfalls, And Practice, Sangil Lee, Joshua Davis, Ken Milne, Christina Shenvi, Lars K. Beattie, Martin Wegman, Laura Melville, Richard D. Shih, Bryan Kane

Department of Emergency Medicine Faculty Papers

No abstract provided.