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
Item Psychometric Assessment Issues and Potential Opportunities(The Healthcare and Life Sciences Symposium (HCLS), co-located with The 2024 Knowledge Graph Conference (KGC), 2024-05-07)Psychometric assessment is essential for mental health care. However, which assessment instruments are best suited for particular use cases remains largely opaque to researchers, clinicians and policy makers alike. Although there have been metrics proposed to indicate the strength of evidence for assessment resources (SOEFA), the reporting of research evidence needed for these metrics is currently inconsistent and highly fragmented, which complicates evaluation of SOEFA. In efforts to improve the systematic collection and reporting of SOEFA, Hunsley and Mash (2008) and Youngstrom et al. (2017) have proposed a standard set of criteria to evaluate the SOEFA. Twelve categories, including norms, internal consistency, interrater reliability, test-retest reliability (stability), repeatability, content validity, construct validity, discriminative validity, prescriptive validity, validity generalization, treatment sensitivity, and clinical utility, are suggested to evaluate mental health assessment resources as adequate, good or excellent. In an effort to apply these criteria to a widely used measure of youth anxiety and depression, the Revised Child Anxiety and Depression Scale (https://rcads.ucla.edu/; Chorpita et al., 2000), we encountered a variety of challenges due to the fit between standards and the knowledge base represented in published research papers. First, it is difficult to map and connect the proposed criteria to the inconsistent, disintegrated and fragmented research evidence, such as varied use of criteria to determine validity or accuracy of measurement. Second, many assessment instruments exist in different versions, such as translations in different languages, derivatives (e.g., short forms) or respondent formats (e.g., youth or caregiver forms). The provenance of different versions (e.g., which items are newly created or are reused from already existing instruments) is highly opaque, and there is minimal guidance about how SOEFA metrics (indicators about the degree of uncertainty in the expected performance of a specific version) can be applied across resources with shared provenance. For example, one could assume that different versions inherit the SOEFA (1) of the parent class of instruments or (2) from sibling classes or (3) or not at all. Third, psychometric assessment instruments are always used in a specific context, i.e., with a specific cohort (with a specified age, gender, language, nationality, etc.) and with a specific purpose (of assessment), i.e., screening, supporting diagnosis, or treatment progress monitoring. To inform end users of the potential suitability of an assessment resource therefore requires marshaling large amounts of meta-data about the contexts and cohorts in which the SOEFA metrics were established for each measure. Thus, despite a laudable aim to apply standardized metrics to inform users about the evidence supporting specific assessment, the practical implementation of these standards (or their evolution) requires a significant change to the knowledge infrastructure of psychometric assessment. In our joint work that includes psychological clinical assessment experts along with semantic technology experts, we are exploring a semantic infrastructure that supports the encoding of SOEFA metrics in the context of mental health screening questionnaires. Our current effort involves the modeling of statistical evidence using standardized terminology from established ontologies (such as HAScO, STATO, and ECO). Our goal is to provide a provenance-aware, semantics-powered data portal that can be used by a broad set of users to help understand some of the important nuances of assessment instruments, to guide which instruments are best suited to which purpose, and to expose the reasons for (or against) such choices, in a way that is aligned with the guidance of the best scholars in mental health assessment.Item Towards an Ontology of Psychometric Measures(The Healthcare and Life Sciences Symposium (HCLS), co-located with The 2024 Knowledge Graph Conference (KGC), 2024-05-07)The field of psychometrics is a subdomain of psychology concerned with assessment of various social and psychological dimensions of humans. In the areas of mental healthcare and the clinical science that supports it, assessment with psychometric instruments remains a dominant strategy for discovery, understanding, and decision-making. This method predominantly relies on text-based distribution of assessment instruments (usually in the form of questionnaires), and significant limitations in terms of interoperability exist. To bridge this gap, we are developing the Psychometric Ontology of Experiences and Measures (POEM) as a domain ontology tailored to capture the relationships and semantics inherent in psychometric instruments. The central aim of this new ontology is to enable the structured representation of psychometric instruments to facilitate standardized assessment across different dimensions and contexts. Other aims are to support the accessibility and construction of instruments, and making machine-readable formats more readily available. POEM considers the connections of observable questionnaire elements and the complex set of latent (unobservable) variables that are the common targets of psychometric assessment. Further POEM supports the tracking of the provenance of assessments at the questionnaire, scale, and item levels in order to capture how assessments are created, used, reused, and translated. The present work is the result of a collaboration of semantic engineers and clinical scientists to develop an ontology for psychometric instruments, adopting a bottom-up approach informed by competency questions and use cases generated by the domain experts in the team to ensure sufficient coverage. The developed ontology is designed to address diverse use cases spanning clinical services, research, and development of new psychometric instruments. POEM builds on the foundation of existing taxonomies and ontologies for psychological disorders like SNOMED-CT, the ICD, and the DSM, and integrates them with semantics describing the internal structure of psychometric assessments. While these existing taxonomies and ontologies include a comprehensive representation of psychological disorders and symptoms, POEM includes the semantics necessary to describe assessments, capturing items, scales, and provenance. POEM’s architecture builds upon established ontologies like the Virtual Solar-Terrestrial Observatory (VSTO), which is a semantic data framework that supports formal representations of physical quantities and their underlying representations with an instrument-focused subset (VSTO-I), and the Human-Aware Science Ontology (HAScO), which supports the description of scientific data for acquisition, preparation, and annotation across a variety of domains. POEM facilitates the integration of observable assessment features, those that describe the architecture of an assessment instrument and its essential parts, with the unobservable semantics of the underlying concepts they represent. The majority of document-level features in POEM align with VSTO-I entities, including questionnaires, questionnaire items, and response options. POEM relates assessments to their underlying concepts on an item-level, relating items to the specific symptoms or constructs they address; more deeply, each response option for an item is shown to estimate a specific experience of the symptom in question. Crucially, POEM also supports the encoding of questionnaire items into scales and subscales, each of which assays a construct (e.g., a clinical syndrome such as depression) that may be present at some level in a human informant, further implying the existence or non-existence of a condition (e.g., a depressive disorder). Our current work focuses on the Revised Children’s Anxiety and Depression Scale (RCADS), which estimates elevations of common psychological disorders and symptoms in children and adolescents, with scales for depression and anxiety, as well as subscales for several specific anxiety disorders [2]. POEM also supports the documentation of the creation and reuse of assessments, scales, and items, including derivation, translation, and related provenance. We evaluate POEM using our competency questions and use case scenarios with regards to the RCADS, an assessment that is suitable for evaluation due to its multiple nested scales, derived versions of different length and target respondents, many translations, and wide usage. Additionally, POEM’s utility is to be demonstrated by its integration into the Semantic Instrument Repository (SIR), a software infrastructure for managing and distributing knowledge graphs related to data acquisition instruments. SIR will harness POEM’s semantic richness to enable semantic search and retrieval functionalities, and facilitate instrument sharing in various formats, including the rendering of questionnaires into human-readable formats. SIR also supports the tracking of the provenance of assessments and their elements, in order to facilitate the creation and reuse of psychometric instruments. We maintain a GitHub repository of the POEM ontology as well as related artifacts and documentation, available at the following: https://github.com/tetherless-world/POEMItem Structural control of photo-curable acrylic resins using photopolymerization-induced phase separation(Rensselaer Polytechnic Institute, Troy, NY, 2023-12)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.Item LLM-Based Code Generation for Querying Temporal Tabular Financial Data(IEEE, 2024-10-22)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.Item Towards a Progression-Aware Autonomous Dialogue Agent(Association for Computational Linguistics, 2022-07-10)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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