DSpace@RPI

DSpace@RPI is a repository of Rensselaer Polytechnic Institute's theses and dissertations which are available in digital format, largely from 2006 to present, along with other selected resources.

Recent Submissions

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    Structural control of photo-curable acrylic resins using photopolymerization-induced phase separation
    (Rensselaer Polytechnic Institute, Troy, NY, 2023-12) Zakrzewski, Lauren, Ashley; Picu, Catalin; Bae, Chulsung
    Heterogeneous thermosetting polymeric material development is of growing interest in the field of material science due to the belief that the development of unique microstructures can provide benefits to the resulting material properties. Advances in these properties have already been explored by the addition of nanoparticles or elastomeric particles into epoxy-based polymer networks. Additional methods of heterogeneous thermoset fabrication include the use of polymer blends, block copolymers, and polymerization-induced phase separation (PIPS). Due to the minimal restrictions on material processing and development, light-initiated PIPS (photo-PIPS) is used in this thesis to fabricate various heterogeneous thermosetting materials through use of phase separation. Photo-PIPS can yield various shapes and sizes of the phase-separated subdomains by controlling multiple parameters including resin composition, phase-separating agent size and concentration, and light intensity. However, the details of the effect of these parameters on the process and the associated mechanisms which govern network evolution are not entirely understood. In the past, studies have been performed to alter the size and concentration of both polymer additives and nanoparticles to visualize the effects on the subdomains that develop. Few additional studies have also been performed on the light intensity effect. In these studies, while the apparent changes to the subdomains are seen by varying the respective parameters, the mechanisms which control the resulting sizes and shapes are still undetermined. Developing an understanding of these governing mechanisms which control network evolution during the phase separation process can help to better alter the extent of phase separation that can occur within a thermosetting network. This in turn, can allow for fine tuning of the material properties and enable the production of materials suited for various applications such as polymer membranes, polymer composites, polymer-dispersed liquid crystals (PDLCs), and the various applications available with stereolithography (SLA) 3D printing. In Chapter 1, an introduction to the creation of heterogeneous materials and more specifically, photo-PIPS is provided, including a short review of the literature pertaining to photo-PIPS which discusses how various microstructures have been obtained. Challenges and potential applications of photo-PIPS are also discussed. A summary of the various parameters which can be altered to adjust the material properties of a thermosetting network is also given. An introduction to the studies presented in the subsequent chapters of the thesis is provided. In Chapter 2, the mechanisms governing network evolution during the photo-PIPS process are determined through use of intermittent light irradiation applied after a period of continuous irradiation to probe network formation at various stages. Specifically, light transmittance experiments and SEM imaging are used to detect the extent and thus, evolution of phase separation. Real-time FTIR is used to examine the impact of intermittent probing on network evolution and profilometry is used to detect network shrinkage. From this chapter, it is learned that phase separation, photoinitiator consumption, and microstructural refinement are the governing mechanisms of network evolution during photo-PIPS. In Chapter 3, photo-PIPS is implemented into two photo-curable resin systems—one with a stiff crosslinking monomer and one with a soft crosslinking monomer—to determine the effect of this parameter on the extent of phase separation and resulting mechanical properties. Light transmittance, SEM, SWAXS, and DMA are all used to verify the extent of phase separation present. It is also indicated through these experiments that there is a dependence on the extent of phase separation with polymer additive molecular weight in both resin systems, which agrees with the literature. Light intensity effects are studied and their results are also in agreement with the literature. Real-time FTIR confirms the kinetics of photopolymerization in both systems and is compared to the kinetics of phase separation provided by light transmittance experiments. Lastly, mechanical property testing is performed to determine the effects of the rigidity of the crosslinking monomer and also of liquid versus solid, linear-chained phase-separated polymer additive on the material properties. The work here provides an understanding on the parameters which can alter the extent of phase separation including polymer additive chemistry and molecular weight, light intensity, and crosslinking monomer rigidity. In Chapter 4, a robust polymer network is developed and used for implementation of photo-PIPS using two different polymer additives: PPG and PDMS, to create two different resin systems and visualize the effects on the material properties. Acrylic-based monomers are photopolymerized to develop the polymer network and epoxy-based monomers within the network are crosslinked during thermal treatment after photopolymerization to produce increased material stiffness and strength. PPG is implemented as the phase-separating agent and the extent of phase separation is monitored via transmittance and SEM, showing the same trend in polymer additive molecular weight as the resin systems of Chapter 3. Mechanical property data show a reduction in stiffness, strength, and elongation due to the liquid nature of PPG at room temperature. In the case of the PDMS polymer additive, due to the phase-separating nature of Epoxy-PDMS and non-phase-separating nature of OH-PDMS, mixtures of OH-PDMS and Epoxy-PDMS of various molar ratios are used to alter the extent of phase separation and fine tune the amount of crosslinking that occurs within the subdomains. Transmittance, SEM, and DMA are used to validate the extent of phase separation and real-time FTIR is used to compare the photopolymerization kinetics with the phase separation kinetics determined from transmittance. It is found that minimal phase separation provides sufficient crosslinking within the subdomains to yield an increase in material ductility and creep resistance, while only slightly reducing the stiffness and strength. Chapter 5 provides conclusionary statements regarding each chapter and their contribution to the current understanding of the photo-PIPS process and the methods that can be used to alter the resulting microstructure and material properties. As a whole, these studies offer insight into the network evolution during photo-PIPS as well as the factors that impact the resulting material such as polymer additive molecular weight and concentration, light intensity, resin composition, and monomer chemistries. It is suggested by these studies that photo-PIPS can provide a vast range of material properties and extents of phase separation, proving to have uses in numerous potential applications. A list of possible future extensions of the present work is provided in closure.
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    LLM-Based Code Generation for Querying Temporal Tabular Financial Data
    (IEEE, 2024-10-22) Lashuel, Mohamed; Kurdistan, Gulrukh; Green, Aaron; Erickson, John S.; Seneviratne, Oshani; Bennett, Kristin P.
    We examine the question of ``how well large language models (LLMs) can answer questions using temporal tabular financial data by generating code?''. Leveraging advanced language models, specifically GPT-4 and Llama 3, we aim to scrutinize and compare their abilities to generate coherent and effective code for Python, R, and SQL based on natural language prompts. We design an experiment to assess the performance of LLMs on natural language prompts on a large temporal financial dataset. We created a set of queries with hand-crafted R code answers. To investigate the strengths and weaknesses of LLMs, each query was created with different factors that characterize the financial meaning of the queries and their complexity. We demonstrate how to create specific zero-shot prompts to generate code to answer natural language queries about temporal financial tabular data. We develop specific system prompts for each language to ensure they correctly answer time-oriented questions. We execute this experiment on two LLMs (GPT-4 and Llama 3), assess if the outputs produced are executable and correct, and assess the efficiency of the produced code for Python, SQL, and R. We find that while LLMs have promising performance, their performance varies greatly across the languages, models, and experimental factors. GPT-4 performs best on Python (95.2\% correctness) but has significantly weaker performance on SQL (87.6\% correctness) and R (79.0\% correctness). Llama 3 is less successful at generating code overall, but it achieves its best results in R (71.4\% correctness). A multi-factor statistical analysis of the results with respect to the defined experimental factors provides further insights into the specific areas of challenge in code generation for each LLM. Our preliminary results on this modest benchmark demonstrate a framework for developing larger, comprehensive, unique benchmarks for both temporal financial tabular data and R code generation. While Python and SQL already have benchmarks, we are filling in the gaps for R. Powerful AI agents for text-to-code generation, as demonstrated in this work, provide a critical capability required for the next-generation AI-based natural language financial intelligence systems and chatbots, directly addressing the complex challenges presented by querying temporal tabular financial data.
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    Towards a Progression-Aware Autonomous Dialogue Agent
    (Association for Computational Linguistics, 2022-07-10) Sanders, Abraham; Strzalkowski, Tomek; Si, Mei; Chang, Albert; Dey, Deepanshu; Braasch, Jonas; Wang, Dakuo
    Recent advances in large-scale language modeling and generation have enabled the creation of dialogue agents that exhibit human-like responses in a wide range of conversational scenarios spanning a diverse set of tasks, from general chit-chat to focused goal-oriented discourse. While these agents excel at generating high-quality responses that are relevant to prior context, they suffer from a lack of awareness of the overall direction in which the conversation is headed, and the likelihood of task success inherent therein. Thus, we propose a framework in which dialogue agents can evaluate the progression of a conversation toward or away from desired outcomes, and use this signal to inform planning for subsequent responses. Our framework is composed of three key elements: (1) the notion of a “global” dialogue state (GDS) space, (2) a task-specific progression function (PF) computed in terms of a conversation’s trajectory through this space, and (3) a planning mechanism based on dialogue rollouts by which an agent may use progression signals to select its next response.
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    Adapting Emotion Detection to Analyze Influence Campaigns on Social Media
    (Association for Computational Linguistics, 2023-07-01) Bhaumik, Ankita; Bernhardt, Andy; Katsios, Gregorios A; Sa, Ning; Strzalkowski, Tomek
    Social media is an extremely potent tool for influencing public opinion, particularly during important events such as elections, pandemics, and national conflicts. Emotions are a crucial aspect of this influence, but detecting them accurately in the political domain is a significant challenge due to the lack of suitable emotion labels and training datasets. In this paper, we present a generalized approach to emotion detection that can be adapted to the political domain with minimal performance sacrifice. Our approach is designed to be easily integrated into existing models without the need for additional training or fine-tuning. We demonstrate the zero-shot and few-shot performance of our model on the 2017 French presidential elections and propose efficient emotion groupings that would aid in effectively analyzing influence campaigns and agendas on social media.
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    Uncovering Agendas: A Novel French & English Dataset for Agenda Detection on Social Media
    (International Committee on Computational Linguistics (ICCL), 2024-05-20) Katsios, Gregorios A; Sa, Ning; Bhaumik, Ankita; Strzalkowski, Tomek
    The behavior and decision making of groups or communities can be dramatically influenced by individuals pushing particular agendas, e.g., to promote or disparage a person or an activity, to call for action, etc.. In the examination of online influence campaigns, particularly those related to important political and social events, scholars often concentrate on identifying the sources responsible for setting and controlling the agenda (e.g., public media). In this article we present a methodology for detecting specific instances of agenda control through social media where annotated data is limited or non-existent. By using a modest corpus of Twitter messages centered on the 2022 French Presidential Elections, we carry out a comprehensive evaluation of various approaches and techniques that can be applied to this problem. Our findings demonstrate that by treating the task as a textual entailment problem, it is possible to overcome the requirement for a large annotated training dataset.

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