Digital Commons Network^™

Decorrelation, Diversity, And Emergent Intelligence: The Isomorphism Between Social Insect Colonies And Ensemble Machine Learning, Ernest Fokoue, Gregory Babbitt, Yuval Levental

Articles

Social insect colonies and ensemble machine learning methods represent two of the most successful examples of decentralized information processing in nature and computation respectively. Here we develop a rigorous mathematical framework demonstrating that ant colony decision-making and random forest learning are isomorphic under a common formalism of stochastic ensemble intelligence. We show that the mechanisms by which genetically identical ants achieve functional differentiation— through stochastic response to local cues and positive feedback—map precisely onto the bootstrap aggregation and random feature subsampling that decorrelate decision trees. Using tools from Bayesian inference, multi-armed bandit theory, and statistical learning theory, we prove that …

A General Weighting Theory For Ensemble Learning: Beyond Variance Reduction Via Spectral And Geometric Structure, Ernest Fokoue

Articles

Ensemble learning is traditionally justified as a variance-reduction strategy, explaining its strong performance for unstable predictors such as decision trees. This explanation, however, does not account for ensembles constructed from intrinsically stable estimators-including smoothing splines, kernel ridge regression, Gaussian process regression, and other regularized reproducing kernel Hilbert space (RKHS) methods whose variance is already tightly controlled by regularization and spectral shrinkage. This paper develops a general weighting theory for ensemble learning that moves beyond classical variance-reduction arguments. We formalize ensembles as linear operators acting on a hypothesis space and endow the space of weighting sequences with geometric and spectral constraints. …

Quantum Indiscernibility And Perspectivalism, Emily Adlam

Mathematics, Physics, and Computer Science Faculty Articles and Research

It has often been suggested that similar quantum particles may be a counterexample to the principle of the Identity of Indiscernibles. In this article, I discuss the status of indiscernibility in a perspectival approach to quantum mechanics. I argue that adopting an internal physical perspective can have the effect of making entities discernible even if they are not discernible relative to external reference frames, and thus perspectivalism offers a new way to understand the physical meaning of weak discernibility. I discuss whether this approach is circular, and I consider whether the reference frames involved are physically meaningful. I conclude that …

Exact Soliton Solutions Of The Nonlinear Time-Fractional Schrödinger Equation Via Atangana–Baleanu And M-Truncated Operators, Bahadır Kopçasiz, Fatma Nur Kaya Sağlam, Mehmet Şenol

Mathematical Modelling and Numerical Simulation with Applications

The main objective of this work is to obtain exact soliton solutions for a nonlinear time-fractional equation model describing wave profiles arising in various physical systems. To derive different wave structures associated with the considered model, two analytical techniques are employed: the extended G'\G^2-expansion method and the modified auxiliary equation (MAE) approach. A wave transformation is applied to reduce the nonlinear time-fractional equation to a nonlinear ordinary differential equation (NLODE) by means of the M-truncated and Atangana-Baleanu (AB) fractional operators. Several classes of solutions, including exponential, hyperbolic, and trigonometric wave forms, are obtained. Over and above the analytical results, graphical …

Learning Ordinal Geometry: Semantic–Aware Kernels For Ordered Categorical Data, Ernest Fokoue

Articles

Ordinal data arise ubiquitously in survey research, psychology, medicine, economics, and recommender systems, yet kernel methods for such data typically rely on either nominal encodings or arbitrary numeric codings. The former discards order information; the lat- ter imposes a fictitious metric structure. This paper develops a principled framework for kernel design on ordinal scales and introduces a new class of Semantic–Aware Ordinal Ker- nels (SAOK) that simultaneously capture ordinal order and semantic proximity between categories. We begin by formalizing order–preserving embeddings of finite chains and characterizing a broad family of chain distances that are conditionally negative definite. Through Schoen- berg …

Projections Of The Characteristics Of Extreme Precipitation In East Africa Using Bias-Corrected Cmip6 Models, Exavery K. Makula

Tanzania Journal of Science

In the context of ongoing global climate change, the intensification of the hydrological cycle is expected to modify the frequency, magnitude, and spatial distribution of extreme precipitation events. Understanding how precipitation extremes will evolve under climate change is critical for East Africa (EA), a region highly vulnerable to hydroclimatic hazards and strongly dependent on rainfall-driven socioeconomic systems. The region experiences two main rainfall seasons, namely the long rains (March-May, MAM) and the short rains (October-December, OND). This study investigates future projections of the characteristics of extreme precipitation across the EA using an ensemble of biascorrected CMIP6 global climate models. Extreme …

Markov-Modulated Queueing Network For Mobile Traffic Aggregation With Threshold-Controlled Buffers, Anton A. Esin, Elmira Yu. Kalimulina

Mathematical Modelling and Numerical Simulation with Applications

We study the problem of data transmission from mobile platforms operating in high-speed transit between cellular base stations, under conditions of unstable and intermittent connectivity. Conventional queueing and connectivity models often fail to capture the combined effects of rapidly changing signal conditions, finite buffer capacity, and dynamic topology. We aim to develop a tractable yet expressive model that integrates stochastic link availability, queue dynamics, and buffer control. We propose a mathematical framework based on queues whose service intensities are modulated by a continuous-time Markov chain (CTMC) representing signal conditions along a high-speed trajectory. The core subsystem is a two-stage (aggregation …

Measuring Market Risk Through Entropic Var, Dragomir Nedeltchev, Tsvetelin Zaevski

Mathematical Modelling and Numerical Simulation with Applications

The article aims to measure the market risk beyond the basic risk measures like the Value-at-Risk (VaR) and the Expected Shortfall (ES). The Entropic Value-at-Risk is selected among the available measures based on its advantages -- it is the coherent upper bound of the VaR and ES. This risk measure is applied to the classical Black-Scholes model as well as to some more realistic ones, such as the exponential tempered stable model (the log-returns are presented by a tempered stable L\'evy process), the stochastic volatility model of Heston, its jump extension of Bates, and another stochastic volatility model but with …

Enhancing Introductory Cybersecurity Learning: A Design-Based Research Case Study, Manny Niri Dr.

Journal of Cybersecurity Education, Research and Practice

This study employs a design-based research (DBR) framework to examine the impact of a comprehensive curriculum redesign in an introductory Foundations of Security module for undergraduate students in computing and cybersecurity at a UK public university between 2019 and 2025. The redesign aimed to enhance student learning, engagement, and critical thinking through the embodiment of evidence-based pedagogical strategies, including flipped classroom delivery, blended learning, gamified practical exercises, repeated low-stakes mock assessments, and structured problem-solving activities. Student feedback, assessment outcomes, attendance records, and faculty reflections were analysed to evaluate the effectiveness of the redesign. The results indicate substantial improvements in student …

Development Of Deep Fused Neural Architecture For Ancient Tamil Palm-Leaf Manuscript Recognition, Hariharan P Mr

Theses and Dissertations

Digitizing Tamil palm-leaf manuscripts is important for education, communication, and the preservation of cultural heritage. The complex structure of the Tamil script, the wide range of handwriting styles, and the degradation seen in ancient Tamil palm-leaf manuscripts make these texts very difficult to read and understand. Digital Image Processing (DIP), document analysis techniques, and traditional Optical Character Recognition (OCR) are unable to handle noise, background interference, faded ink, and limited labelled data, motivating the need for robust, effective Deep Learning (DL)- based solutions.

As a prerequisite to understanding and designing effective recognition systems for ancient manuscripts, this thesis first examines …

Patterns Of Multimorbidity Among Low-Income Adults Who Smoke With Implications For Tailored Interventions: A Cluster Analysis Using A Mixture Of Bernoulli Model, Monique T. Cano, Michael R. Lindstrom, Oscar F. Rojas Perez, Ricardo F. Muñoz

School of Mathematical & Statistical Sciences Faculty Publications

Introduction: Smoking cigarettes remains a leading modifiable risk factor for preventable health conditions. In the United States, the health burden of smoking disproportionately impacts low-income individuals. Multimorbidity is common in this group, complicating treatment and worsening outcomes. Identifying multimorbidity clusters can support targeted, individualized interventions. This study aimed to identify multimorbidity clusters among individuals who smoke and experience economic hardship and provide clinical recommendations to enhance health outcomes.

Method: Individuals who smoke and experience economic hardship (N = 60) were recruited from the San Francisco Health Network (SFHN) and were assessed for physical and mental conditions. Cluster analysis was …

The Core-Modulation Architecture (Cma): A Structural Overview Of A 14-Paper Research Program (Preprint), Griselda Poe

Publications and Research

This document provides a structural overview of the Core-Modulation Architecture (CMA), a 14-paper research program on cognition, communication, and AI interaction.

The series specifies the conditions under which cognition operates, terminates, fails, and generates structure. Rather than describing cognition by its contents (beliefs, emotions, decisions), it defines cognition through its underlying architecture: constraint-governed processing across layers with distinct termination conditions.

The framework introduces a layered model consisting of Core processing (constraint preservation and structural coherence) and Modulation (affective calibration and social interface adjustment), extended by a Prior layer as the source of constraints. Across the series, phenomena such as miscommunication, …

Axial Sulfur-Coordination Engineering Boosting Fe–N–C Catalysts For High-Performance Proton Exchange Membrane Fuel Cells, Lin Lin, Xiu-Xuan Hou, Zhe-Chen Fan, Yi-Xuan Yin, Wei-Yi Zhao, Kai Wei, Yu-Die Zhou, Li-Na Hou, Ying Wang, Hao Wan, Jun-Jie Ge

Journal of Electrochemistry

Fe-N-C catalysts have long suffered from kinetically sluggish oxygen reduction reaction (ORR) due to excessive adsorption strength toward oxygen intermediates and low site utilization. Heteroatom doping effectively accelerates ORR reaction kinetics through electronic structure modulation of metal sites for optimal intermediate adsorption, while chemical vapor deposition (CVD) enhances the turnover frequency (TOF) of active sites. Herein, we developed an FeSNC catalyst featuring abundant FeS₁N₄ sites via a dual-precursor CVD strategy. Experimental and theoretical analyses revealed that S incorporation disrupts the symmetric coordination of active sites, which optimizes OH* adsorption energies from 0.212 eV to 1.194 eV. Moreover, …

Entropy Of Metal/Solution Interfaces: Thermodynamic Framework, Theoretical Models, And Roles Of Interfacial Water, Zeng-Ming Zhang, Zi-Xi Liang, Jun Huang

Journal of Electrochemistry

Entropy is a basic thermodynamic property of the electrical double layer (EDL) at metal/solution interfaces, yet, its definition, measurement, and theoretical treatment are dispersed in the literature, and, in some cases, ambiguous. In this paper, we revisit the thermodynamic theory of EDL, from which two variants of entropy, excess entropy and formation entropy, are obtained and compared. In terms of the formation entropy, two calculation routes are validated in the context of a primitive EDL model, namely, the Gouy-Chapman (GC) model. After clarifying the concepts and calculation routes, we investigate interfacial water effects on the EDL entropy, using a refined …

Classification Of Land Cover In Sentinel-2 Imagery Using Machine Learning Models, Ehsan Ali Al-Zubaidi, Mohammed Ridha Hammoodi, Ahmed Naser Alzurfi

Al-Bahir

Remote sensing data of medium resolution are commonly used to classify land cover, and machine learning (ML) models have taken on a central aspect in the necessary data analysis. Ordinarily, land cover is coded on a pixel basis on the basis of Digital Number (DN) values, which in turn are computed across several spectral bands. This paper is concerned with land cover mapping in Mosul, Iraq, based on satellite images captured by Sentinel-2. Two platforms featuring unsupervised classification algorithms were used, Google Earth Engine and ArcMap, making it possible to use K-means and X-means in Google Earth Engine and ISO …

A Study Of Transients From Ground-Based Surveys Reveals New Ultra-Compact Accreting White Dwarf Binaries, Jan Kára, Liliana E. Rivera Sandoval, Wendy Mendoza, Thomas J. Maccarone, Manuel Pichardo Marcano, Luis Salazar Manzano, Ryan J. Oelkers, Jan Van Roestel

Physics & Astronomy Faculty Publications

AM CVn stars are ultra-compact semi-detached binaries consisting of a white dwarf primary and a hydrogen-depleted secondary. In this paper, we present spectroscopic and photometric results of 15 transient sources pre-classified as AM CVn candidates. Our analysis confirms 9 systems of the type AM CVn, 3 hydrogen-rich cataclysmic variables (accreting white dwarfs with near-main-sequence stars for donors), and 3 systems that could be evolved cataclysmic variables. Eight of the AM CVn stars are analysed spectroscopically for the first time, which increases the number of spectroscopically confirmed AM CVns by about 10%. TESS data revealed the orbital period of the AM …

Precision Spectroscopy Of 2s-Ns Transitions In Atomic Hydrogen: A Determination Of The Proton Charge Radius, R. G. Bullis, W. L. Tavis, M. R. Weiss, Orellana O. Cisneros, A. J. Cheeseman, Ulrich D. Jentschura, D. C. Yost

Physics Faculty Research & Creative Works

We present absolute frequency measurements of 2S_1/2-nS_1/2 two-photon transitions with n=8, 9, and 10 in a cryogenic beam of atomic hydrogen. Each transition has been measured with a fractional uncertainty of ≈2.6x10^-12. Combining the results from this Letter and the 1S_1/2-2S_1/2 transition frequency, we extract a root-mean-square proton radius of rp=0.8433(31) fm and a Rydberg frequency of cR_∞=3 289 841 960 252.9(9.7) kHz. These are in good agreement with the CODATA 2022 recommended values.

Interview With Orca Award Winner David Hage, Unl Chemistry; Drug-Protein Interactions, Mark Griep, David Hage

UNL ORCA Interview Series

Prof. David Hage was awarded the ORCA in 2018. He was born in La Crosse, Wisconsin, and earned a bachelor’s in chemistry at the University of Wisconsin in La Crosse. Next, he earned a doctorate from Iowa State University and did postdoctoral work at the Mayo Clinic in Rochester, Minnesota. In 1989, he joined UNL as an assistant professor of chemistry and quickly rose through the ranks. He has won many awards and, for the past 13 years, has been the James Hewet University Professor of Chemistry. Allow me to share some remarkable facts about Dave before we get into …

The Waldo Dataset, Mary E. Koone, Rosie Kallie, Vassilis Athisos, Laurel S. Stvan

Computer Science and Engineering Datasets - Archive

Distinct from the task of predicting the author of a document (authorship attribution), we focus on addressing the issue of how to estimate the similarity between the written language styles of authors. To do so, we present a dataset of metadata derived by asking human annotators, who were presented with three documents, to identify which two were written by the same author and which was written by a different author. The dataset has over 400 such annotations, creating a companion to the Amazon Web Services (AWS) customer review dataset, laying the groundwork for crowdsourcing applications to other natural language processing …

Role Of Wadsley Defects And Cation Disorder To Enhance Monb12O33 Diffusion, Cj Sturgill, Manish Kumar, Nima Karimitari, Iva Millisavljevic, Coby S. Collins, Aaron Hegler, Hsin-Yun Joy Chao, Santosh Kiran Balijepalli, Scott T. Misture, Christopher Sutton, Morgan Stefik

Faculty Publications

Wadsley-Roth (WR) niobates have emerged as high-rate anode materials that can combine rapid ionic diffusion with good electronic conductivity. WR compounds have been defect-enhanced by limited annealing, however, such materials often contain multiple types of defects. In particular, both Wadsley defects (variable block size) and transition metal disorder have the potential to modify transport rates, however the corresponding effects are not well understood mechanistically. Here, MoNb12O33 (MNO) was calcined at two different temperatures to compare a defect-rich condition (MNO-800) with a proximal order-rich condition (MNO-900) as assessed through XRD, XANES, EXAFS, and STEM characterizations. Galvanostatically cycled lithium half cells of …

Effect Of Light Elements On The Melting Behavior Of Liquid Iron Alloys Under Earth’S Inner Core Boundary Conditions, Olaniyi Anisere

LSU Master's Theses

The temperature at Earth’s inner core boundary (ICB) is set by the melting temperature of the iron-rich outer core at core pressures, yet this remains uncertain because melting depends strongly on composition and pressure. Light elements are required to explain the core’s density deficit, but the identity, abundance, and combined influence of these elements on melting under ICB conditions are still debated. This thesis quantifies how candidate light elements modify the melting behavior of iron alloys at ICB-relevant pressures and uses these composition-dependent melting relations to constrain ICB temperature.

Melting relations were determined using molecular dynamics simulations in VASP accelerated …

Properties Of The Graph Modularity Matrix And Its Applications, Benjamin Quiring, Panayot S. Vassilevski

Mathematics and Statistics Faculty Publications and Presentations

We study the popular modularity matrix and respective functional ([20]) used in connection with graph clustering and derive some properties useful when performing vertex aggregation of the associated graph. These properties are employed in the derivation of a multilevel parallel pairwise aggregation algorithm. Comparative performance results of the studied algorithm applied to graph clustering tested against the popular Louvain algorithm [4], [3] are presented. Some illustrative examples show that the resulting aggregates if used in an adaptive algebraic multigrid (AMG) are able to follow strong direction of anisotropy in finite element problems. 1.

Curriculum For A Two Semester Calculus Course Specializing In Life Science And Data Science, Patrick Mcclain

LSU Master's Theses

Traditionally, introductory calculus has been designed for engineering and physics students, often leaving students majoring in data science or life sciences with a curriculum that lacks professional relevance and is overly reliant on problems that focus on computational fluency. This thesis proposes a two-semester sequence, called MATH 153X and MATH 154X, specifically tailored for the Louisiana State University (LSU) Dual Enrollment program and university-level data science and life sciences majors. By integrating modern computational tools—such as symbolic calculators and artificial intelligence (AI) tools—the proposed curriculum shifts the pedagogical focus from procedural symbolic manipulation toward conceptual literacy.

Through a series …

Geospatial Understanding Of Foundation Models: Self-Supervision, Robustness, And Resource-Constrained Learning, Gilbert Kibet Rotich

USF Tampa Graduate Theses and Dissertations

Research on large-scale geospatial understanding from satellite imagery remains relativelyunderexplored. Much of the field still relies on narrow, task-specific pipelines, and there is lim- ited work on developing true foundation models or on systematically evaluating how well these models generalize under domain shift, geographic variation, and real-world scene diversity. This dissertation advances foundation-model pretraining and evaluation for remote sensing along three complementary axes. First, we develop self-supervised multispectral pretrained models for satellite image classification by adapting BYOL, BarlowTwins, DINO, MoCo, and Simsiam to multispectral inputs, addressing the limited amount of curated satellite datasets relative to ImageNet. Pretraining on Eurosat and …

Meteorological Monitoring With Real-Time Data Processing Using Uas, Htoo Poe Sar, Cal Coopmans, Sierra Young

Spring Runoff Conference

Background and Motivation 

• Agricultural water management requires information about crop water use and water consumption 
• Remote sensing methods are used to model and estimate environmental processes – but they require weather data 

First-Order Axial Perturbation Of The Reissner–Nordström Metric Against A Possible Parity-Violating Gravity Background, Abhishek Rout, Brett David Altschul

Faculty Publications

We study axial perturbations of Reissner–Nordström black holes within the general frame-work of parity-violating modified gravity theories. We derive the governing equations for a class of frame-dragging perturbations, focusing on the symmetry structure and radial dependence of the perturbed metric component, describing its behavior across three distinct regions: near the singularity (r → 0), between the inner and outer Reissner–Nordström horizons (r− < r < r+), and in the asymptotic exterior regime (r → ∞). Using a combination of analytical and numerical methods, we analyze the solutions for varying black hole charge-to-mass ratios (Q/M) and angular momentum parameters (l). Key findings include the suppression of perturbations by the electromagnetic field for higher Q/M; the emergence of radial resonance-like behavior for specific l values; and a high degree of symmetry for solutions in the extremal limit (Q/M ∼ 1), attributed to the AdS2 × S 2 near-horizon geometry. The WKB approximation is employed to study the high-l regime, revealing quantized radial resonance modes and singular behavior in the extremal limit. Additionally, we explore the role of boundary conditions and the possibility of a Chern–Simons field Θ as the source of the parity violation, showing that consistency and the behavior of the perturbations under time reversal demand a constant field (and thus no actually observable Chern–Simons effects) at leading order. These results provide a basis for further analysis of the stability and dynamical properties of charged black holes in parity-violating theories, with potential experimental signatures in gravitational wave observations.

Investigation Of Molecular Footprints In Ultra-High Diluted Homoeopathic Solutions Of Arnica Montana And Rhus Toxicodendron Through Combined Infrared And Impedance Spectroscopy, Sharbadeb Kundu, Usha Rani Dash, Chirasree Roychaudhuri, Aindrila Roy, Sanatan Chattopadhyay, Rathin Chakravarty, Debadatta Nayak, Subhash Kaushik, Amit Roy Chowdhury, Ananya Barui, Subhadip Chakraborty

Indian Journal of Research in Homoeopathy

Background: The study of ultra-high diluted (UHD) solutions is a compelling and controversial domain in modern science. Despite numerous investigations and hypotheses, there is a lack of rigorous, reproducible empirical evidence supporting the existence of molecular footprints beyond the Avogadro's limit. Objective: This study conducted a detailed investigation of the molecular and electrical properties, focusing on hydrogen bonding and impedance to understand the fundamental nature of UHD solutions. Materials and methods: Combined impedance and infrared spectroscopic investigations were performed to probe the structural behaviour of relevant water networks at the molecular level of UHD alcoholic extract of Arnica montana (AM) …

A Surface Wave Inversion Method Incorporating Interface Constraints And Its Application In The Assessment Of Ground Vibrations Caused By Mining-Induced Seismicity, Li Cheng, Qian Changbao, Liu Ying, Cheng Yi, Ni Shengjun, Yao Huajian

Coal Geology & Exploration

Objective In mining areas of western China, underlying interfaces with significant fluctuations affect seismic exploration accuracy while also posing challenges to the assessment of ground vibrations triggered by dynamic hazards such as mining-induced seismicity. To enhance the characterization of shallow velocity structures and to provide support for dynamic hazard prevention and control in mining areas, this study developed an inversion method incorporating prior information about geological interfaces.Methods A dense array consisting of 489 single-component and 108 three-component seismometers was deployed within a mining area in Longkou Town, Junggar Banner, Inner Mongolia. Accordingly, continuous ambient noise data for about one …

Secure Machine Learning In Networking Systems, Wenwei Zhao

USF Tampa Graduate Theses and Dissertations

With the rapid integration of Machine Learning (ML) into networking systems, ensuring the security and trustworthiness of these intelligent frameworks has become a paramount concern. While ML offers unprecedented capabilities in spectrum management and collaborative learning, it also introduces novel vulnerabilities that can be exploited by sophisticated adversaries. This dissertation investigates and addresses critical security challenges across three key dimensions of ML-driven networking: adversarial spectrum sensing, the security of privacy-preserving unlearning processes, and efficient post-attack model recovery.

First, we address the threat of adversarial spectrum attacks in cognitive radio networks, where malicious nodes manipulate sensing reports to disrupt spectrum access. …

Butte Priority Soils Operable Unit Diggings East Final 95% Remedial Design Package Submittal, Josh Bryson

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.