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Search for the CSS Tallahassee
No full textIn August 1864, the Confederate States cruiser, C. S. S. Tallahassee, conducted an 18-day raiding patrol off the north Atlantic coast. Once Tallahassee was detected, the Union Navy initiated a futile naval campaign to intercept and capture or destroy her. Secretary of the Navy, Gideon Welles, ordered all available ships from the Boston, New York and Philadelphia Navy Yards and Hampton Roads to put to sea in order to neutralize Tallahassee. This account details the ensuing action of the search for Tallahassee
The Catalyst: UIS Research Review, Issue 3
No full textThe Catalyst is a publication by the Research Society at UIS that highlights student research at the university. This issue includes Hamza Azhar and his research with Md Rasel Al Mamun, Phishing Attack Protection Motivation
Review essay--Ideology and the Curriculum
No full textEssay-review of Michael Apple, "Ideology and the Curriculum" published by Routledge & Kegan Paul, 198
Use of electrical impedance spectroscopy (EIS) for diagnosis and prediction of reproductive status in female swine
Get PDFSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo termsThe student, Pastor Vilchez Herrera, accepted the attached license on 2025-02-10 at 04:51.The student, Pastor Vilchez Herrera, submitted this Thesis for approval on 2025-02-10 at 14:52.This Thesis was approved for publication on 2025-02-18 at 10:17.DSpace SAF Submission Ingestion Package generated from Vireo submission #21639 on 2025-10-19 at 18:09:04Efficient management of fertility within the swine breeding herd is vital for maximizing productivity. Key reproductive events, such as estrus detection, pregnancy confirmation, and farrowing, must be effectively managed to optimize animal flow and pig production. Currently, few tools are available to accurately and quickly diagnose and predict reproductive events and instead rely upon labor to identify the outcome. Electrical impedance spectroscopy (EIS) employs a range of frequencies to measure changes in tissues responses to an applied charge. The objective of this research was to evaluate EIS as a tool for diagnosing and predicting reproductive events in swine. Impedance readings were obtained intravaginally using an EIS device equipped with a four-electrode transducer and an internal processor, measuring tissue impedance (Ω) and phase across a frequency range of 1000-29000 Hz and 10-400 ohms. The device was connected to a mobile unit for wireless data collection and storage. Prior to scanning each sow, the device was cleaned and disinfected before insertion into the vagina. Once in the vagina, readings were obtained within ~15 seconds. One sow could be scanned every 90 seconds when in adjacent stalls. Experiment 1 tested EIS to identify estrus in weaned sows (n=546) that were scanned from the day before weaning until day 6 post-weaning. Experiment 2 tested EIS as a method for early determination of pregnancy with scanning on days 1, 10, and 19 in weaned sows (n=500) following insemination. Experiment 3 used EIS to predict the day of farrowing in sows (n=503) from day 113 of gestation until the day of farrowing. Following the collection of data, EIS readings were automatically uploaded to a dedicated website for processing and analysis. Data were analyzed in RStudio and Python to develop and evaluate impedance patterns by day and models for the prediction of reproductive events. Our results indicate that the prediction and identification of estrus within 36 hours in weaned sows had an F1 score = 0.89 for no estrus and 0.76 for estrus detection, with a balanced accuracy of 0.85. For diagnosing pregnancy, the model attained an F1 score = 0.86 for non-pregnant sows and 0.52 for pregnant sows, with a balanced accuracy of 0.78 on day 19. For farrowing, the model reached an F1 score = 0.88 for farrowing not occurring and 0.70 for farrowing occurring within the next 36 hours, with a balanced accuracy of 0.83. Our results suggest that various frequencies can detect changes in the vaginal impedance in the days before a reproductive event. Our efforts continue in impedance pattern analysis by day and frequency as well as developing predictive and machine learning models to improve the accuracy and potential for practical use of this technology in swine herd management
Modeling and editing 4D scenes by leveraging structural priors
Get PDFSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo termsThe student, Jipeng Lyu, accepted the attached license on 2025-04-21 at 17:19.The student, Jipeng Lyu, submitted this Thesis for approval on 2025-04-21 at 17:36.This Thesis was approved for publication on 2025-04-22 at 15:04.DSpace SAF Submission Ingestion Package generated from Vireo submission #21702 on 2025-10-19 at 18:09:134D scene understanding powers applications ranging from AR/VR and robotics to controllable video generation. The goal is to build representations that can faithfully model and manipulate real-world environments as they evolve over time—capturing both how scenes deform (modeling) and how they respond to high-level user instructions (editing). A popular approach is to represent scenes using compact geometric primitives such as 3D Gaussians, which enable efficient rendering and temporal consistency across frames. While recent advances in 3D reconstruction and video editing have shown promising results, many existing methods still overlook a key aspect: structure. Whether in the form of spatial rigidity or semantic hierarchy, structural priors are both abundant and underexplored. This thesis investigates how incorporating such priors—geometric and semantic—into 4D scene modeling and editing can enhance efficiency, controllability, and generalization. The first part of this thesis focuses on geometric structural priors in dynamic 3D modeling. Many dynamic scenes exhibit coherent change patterns: objects often deform in groups, move rigidly or semi-rigidly, or follow interpretable part-wise trajectories. Instead of modeling motion independently for each element, we propose a structural cascaded optimization framework that organizes 3D Gaussians into a coarse-to-fine hierarchy. This structure allows us to parameterize deformation using simple transformations—rotation, translation, and scaling—substantially accelerating optimization. It also enables dense point tracking and motion-based segmentation without requiring semantic labels. These results demonstrate the potential of structured representations for fast and interpretable 4D scene modeling. The second part explores semantic structural priors in video editing. User instructions often involve multiple entangled goals that are difficult to fulfill through a single transformation. To address this, we employ large language models (LLMs) to decompose complex prompts into interpretable semantic subgoals. Each subgoal defines an editing stage, executed within a training-free diffusion-based video editing framework. To accommodate varying subgoal complexity, we further prompt the LLM to estimate editing difficulty and adapt the interpolation schedule accordingly. This results in smoother transitions and robust edits, transforming the process into a semantically grounded and interpretable sequence. Together, these contributions highlight the value of structural reasoning in 4D scene understanding. By bridging geometric modeling and semantic editing, this thesis offers unified insights into building efficient, robust, and controllable 4D systems guided by structural priors
Design of a grid-tied inverter and LCL filter for photovoltaic application
Get PDFSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo termsThe student, Victoria Jeziorczak, accepted the attached license on 2025-05-07 at 14:11.The student, Victoria Jeziorczak, submitted this Thesis for approval on 2025-05-07 at 14:25.This Thesis was approved for publication on 2025-05-09 at 14:34.DSpace SAF Submission Ingestion Package generated from Vireo submission #21723 on 2025-10-19 at 18:09:17Grid-tied inverters inject harmonics into the grid when using high-frequency PWM, reducing the grid’s energy quality. Therefore, a filter is required between the power inverter and the utility grid to ensure quality energy that abides by IEEE standards for grid integration. This paper proposes a 4kW, 400V grid-tied inverter and LCL filter design to interface an array of solar panels to the utility grid. The theory of the two-level, three-phase power inverter and its filter, along with their corresponding designs, is analyzed. Filter selection is discussed, in addition to a filter damping method using a resistor in series. Design, simulation, and experimental tests demonstrate the mathematical theory of the selected LCL filter
Deep learning-based M-mode OCT system and B-mode OCT system diagnosis accuracy comparison
Get PDFSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo termsThe student, Linjie Tong, accepted the attached license on 2025-04-28 at 22:28.The student, Linjie Tong, submitted this Thesis for approval on 2025-04-28 at 22:34.This Thesis was approved for publication on 2025-04-30 at 16:35.DSpace SAF Submission Ingestion Package generated from Vireo submission #21725 on 2025-10-19 at 18:09:17M-mode Optical Coherence Tomography (OCT) imaging is a cost-effective alternative to the widely used B-mode OCT in medical imaging. However, as an emerging imaging modality, M-mode OCT has not been extensively studied for its diagnostic capabilities. In this paper, we propose a convolutional neural network (CNN)-based framework to evaluate the diagnostic performance of M-mode and B-mode OCT images. Our results demonstrate that M-mode OCT can achieve comparable diagnostic accuracy to B-mode OCT. To investigate the reason behind this comparable performance, we conduct further analysis in two parts. First, using transfer learning, we show that deep learning models extract highly similar features from both M-mode and B-mode OCT images. Second, we analyze the feature distributions and observe that both modalities yield distinguishable differences between normal and abnormal cases. These findings suggest that the critical diagnostic information in OCT images is primarily encoded in the depth profiles of individual A-scans. Motivated by this insight, we propose a weakly supervised algorithm based on Multi-Instance Learning (MIL), which extracts features from individual A-scans and integrates them to generate final diagnostic predictions. Notably, this method does not require per A-scan labels during training, yet it is capable of producing per A-scan predictions. The proposed approach achieves diagnostic performance comparable to models that utilize entire M-mode or B-mode OCT images, while offering enhanced interpretability through localized, per A-scan outputs
Assessing trustworthiness of neural networks for computer systems
Get PDFSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo termsThe student, Isha Chaudhary, accepted the attached license on 2025-04-10 at 12:44.The student, Isha Chaudhary, submitted this Thesis for approval on 2025-04-10 at 12:48.This Thesis was approved for publication on 2025-04-11 at 09:40.DSpace SAF Submission Ingestion Package generated from Vireo submission #21740 on 2025-10-19 at 18:09:18Neural networks have exhibited superior performance in several domains such as computer vision, natural language processing, etc. They have also been trained as computer system components for performance improvement. Although they deliver expected performance, they are not trusted by domain experts, as they are black-box functions and can show unexpected behaviors. This is particularly important for computer systems, as any unpredictable errors in one component can lead to catastrophic failure of the entire system, which is often deployed in consumer-facing applications. Hence, it becomes imperative to develop custom methods to evaluate and develop trust in neural network components of computer systems. In this thesis, I will describe a couple of frameworks that I have developed to address the issue of trust of neural networks in computer systems. The first framework, COMET, is an explanation framework to generate faithful explanations for the predictions of neural networks deployed as performance/cost models in compilers. Cost models statically predict the cost of execution of a given piece of code on a specific CPU. Their predictions are used to direct the compiler optimizations towards the most effective transformations of the code, hence making them crucial for effective compilation. Recent research has developed neural cost models. However, they are not considered trustworthy, as they are black-boxes with no insight into what led to their predictions. Our framework, COMET, is the first step towards mitigating this concern. COMET generates explanations for the prediction of any given cost model for an input code, as a subset of the features of the code that are sufficient to result in the prediction. With explanations, incorrect behaviors of the black-box cost models can be debugged and the trust of the domain experts can be achieved. The second framework, SpecTRA is an automated specification generation system for neural networks as computer system components. We formulate specification generation as an optimization problem and solve it with observations of expected behaviors. We hypothesize that the traditional (aka reference) algorithms that neural networks replace for higher performance can act as effective proxies for expected correct behaviors of the models, when available. SpecTRA clusters similar observations into compact specifications. We present specifications generated by SpecTRA for neural networks in adaptive bit rate and congestion control algorithms. Our specifications show evidence of being correct and matching intuition. Moreover, we use our specifications to show several previously unknown vulnerabilities in SOTA neural models for computer systems
Efficient static checking of safety properties in concurrent smart environments
Get PDFSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo termsThe student, Rishabh Menezes, accepted the attached license on 2025-04-17 at 00:02.The student, Rishabh Menezes, submitted this Thesis for approval on 2025-04-17 at 22:12.This Thesis was approved for publication on 2025-04-18 at 10:54.DSpace SAF Submission Ingestion Package generated from Vireo submission #21811 on 2025-10-19 at 18:09:30The Internet of Things (IoT) space in smart environments, including homes and buildings, presents key challenges in the realm of automation safety. Users can control smart devices with routines, or command sequences that operate serially individually but out of order with respect to each other. While this concurrency can be useful for parallelizing tasks, it can also make the smart environment susceptible to entering undesirable or unpredictable states. As smart device effects have real-world consequences for end users and their property, preventing aberrant executions is of critical importance. This thesis presents verification methods for the static version of this problem, where safety is checked at routine submission time. It will contribute (i) a method of safety specification, by which users can express safety conditions to be held as invariants, and (ii) solutions for static checking of routines against specified safety conditions. As the latter problem is NP-hard, the thesis first presents a system of algorithms that demonstrate model-specific optimizations which improve runtime performance through a conservative approach to safety, provably catching 100% of safety violations. The thesis secondly contributes a generic formal model of the smart environment in a popular specification and verification language called Maude, which enables users to model check a given configuration of devices and routines against safety conditions to find any violations
Ideological framing in Taiwanese media: Analyzing editorial responses to COVID-19 incidents
Get PDFSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo termsThe student, Jillian Keng, accepted the attached license on 2025-04-17 at 14:18.The student, Jillian Keng, submitted this Thesis for approval on 2025-04-17 at 14:31.This Thesis was approved for publication on 2025-04-17 at 15:42.DSpace SAF Submission Ingestion Package generated from Vireo submission #21824 on 2025-10-19 at 18:09:32This study critically analyzes the editorials of two Taiwanese newspapers, the China Times and the Taipei Times, regarding their portrayal of three incidents that occurred during COVID-19, which led to domestic political disputes. These incidents include Taiwan's exclusion from the WHA meeting, the ruling Democratic Progressive Party (DPP) government's mask diplomacy, and the debate surrounding the potential renaming of China Airlines. The selected articles have been translated from Mandarin to English. The theoretical framework for this study utilizes the five generic news frames proposed by Semetko and Valkenburg (2000), the concept of textual silence by Huckin (2002), and the emphasis and de-emphasis strategies identified by Rosulek (2014). The analysis examines how the publishers frame the events, the information presented to readers, the information excluded, and what is given greater emphasis. This approach helps to identify how the newspapers uphold their ideological stances through various linguistic techniques. The findings indicate that the China Times frequently frames events by suggesting misconduct by the current government. At the same time, the Taipei Times tends to frame incidents in terms of moral obligations or emotional appeals to foster empathy and encourage timely governmental action. Furthermore, the China Times includes and emphasizes cross-strait relations or international risks, often excluding or downplaying Taiwan's success in managing COVID-19 and gaining international recognition. Conversely, the Taipei Times highlights Taiwan's contributions during the COVID-19 pandemic, providing more detailed information on these topics. However, their editorials rarely address the political constraints and potential repercussions that may arise from advocating for national identity recognition. These insights underscore the need for further research to uncover the underlying ideologies in newspaper editorials, highlighting the pervasive nature of media bias and the importance of media literacy