VictorJuiz/thesis huggingface dataset:
X-Ray Vision at work : diagnosing covert diversity, equity & inclusion dynamics in a non-profit organization
Keywords: Humanities and Sciences.

This qualitative case study seeks to understand, from a systems psychodynamics perspective, how employees experience covert dynamics during organizational development and change (ODC) initiatives that are focused on improving diversity, equity, and inclusion (DEI) in the workplace. Using the organizational framework and diagnostic tool "X-Ray Vision," I explored with twenty-seven nonprofit employees the application and effectiveness of the tool as they examined covert DEI dynamics that were influencing implementation of their strategic plan. Specifically, I wanted to know how ODC and DEI researchers and practitioners can use X-Ray Vision as a diagnostic tool to support employees while they are designing, leading, and managing change. Study participants were chosen using a convenience sampling methodology. The primary data collection methods were focus groups and semi-structured interviews, supported by participant-observation and document review. The data were organized, coded, and analyzed using the X-Ray Vision conceptual framework and related concepts in the DEI literature. The research revealed that there were many covert DEI dynamics operating at multiple levels of the organizational system  intrapersonal, interpersonal, group, intergroup, and interorganizational  that were having a negative impact on employee well-being, relationships, motivation, and job satisfaction, as well as organizational culture, climate, and performance. The study's findings suggest that the X-Ray Vision diagnosis is most useful when the researcher or practitioner explicitly links covert concepts in the tool with related DEI concepts and expertly uses self-as-instrument when engaged in DEI-specific organization development and change challenges. The study offers recommendations for ODC and DEI researchers and practitioners who want to diagnose covert DEI dynamics operating beneath the surface of organizational life.
3D Printing Lattice Based Pressure Sensors for Human Health and Performance Monitoring
Keywords: 3D printing
Dielectrics
Capacitive pressure sensors
Text
Thesis

Capacitive pressure sensors have generated significant interest for applications in human health and performance monitoring. A wide range of potential applications require low cost, compliant sensors that can be tuned to appropriate sensing regimes and sensitivities. Previous literature has shown that microstructuring the dielectric layers of sensors can provide significant increases in sensitivity and that dielectric geometry can directly impact sensor range and performance. Leveraging advances in high resolution 3D printing we have developed a new method for fabricating elastomeric lattices with highly tunable geometric properties for use as capacitive pressure sensor dielectrics. We characterized the impact of microstructure geometry on the capacitive pressure sensors’ mechanical performance and developed a new set of analytical equations suitable for the lattice geometries to predict the capacitance response and sensitives of microstructured sensors under external pressure. This work covers 1) design rationale of lattice unit cells, 2) an analytical expression that directly predicts the capacitance response, and 3) 3D printing elastomeric lattice dielectric layers, which taken together serve as a framework and starting point to design and fabricate capacitive pressure sensors for various biomedical applications as well as for human health and performance monitoring.
3KG v2: Universal Electrocardiogram Representations for Label-Efficient Phenotype Discovery
Keywords: Rajpurkar, Pranav
Machine learning
Deep learning (Machine learning)
Artificial intelligence
Cardiology
Electrocardiography
Electrocardiography > Interpretation
Echocardiography
Echocardiography > Digital techniques
Few-shot Learning
Phenotype Discovery
Universal Representations
Text
Thesis

We propose 3KG v2, a new self-supervised learning method for universal representation learning of electrocardiograms. This method builds upon its predecessor by featuring a new contrastive objective and transformation space. We assess the quality of representations generated by this algorithm by transferring pre- trained models from a large public dataset to various downstream tasks, including some with- out prior clinical association with ECGs. Performance evaluation is conducted in both few- shot and full data settings to account for limited and complete training data availability, respectively. For each task, we perform a linear evaluation to assess the effectiveness of the pretrained representations. For tasks in the full- data setting, we additionally perform a full-fine tuning to determine the performance ceiling of each method in a practical deployment scenario.
Our results demonstrate that 3KG v2 consistently outperforms a randomly initialized model trained solely on the target task across all downstream tasks and settings. Specifically, we achieve state-of-the-art performance in few- shot diagnosis of right ventricular function and aortic valve stenosis, two conditions that typically require large amounts of labeled ECGs for effective model training. Moreover, we find that fine-tuning 3KG v2 on our source task’s labels can lead to exceptional transfer capability across a variety of tasks. Notably, our model demonstrates a high level of accuracy in predicting left atrial volume index, achieving a 0.720 C-index even in a few-shot setting. This achievement appears to be unprecedented, as we are not aware of any other ECG model that performs well on this task. While 3KG v2 out- performs our baselines and shows promising results on the majority of phenotype discovery tasks, there is still room for improvement in the absolute performance of any ECG model on many of these complex tasks. Further research is warranted to continue developing and improving such methods for phenotype discovery tasks on ECGs.
A Computational Analysis of Inter-Authorial Influence Within Fanfiction Communities
Keywords: fanfiction
Cosine Similarity
Natural language processing (Computer science)
Influence (Literary, artistic, etc.)
Fan magazines
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Thesis

Although fanfiction is built on existing media properties, its reliance on shared worldbuilding and inspiration makes patterns of influence much more apparent within given communities than in other media environments; some call this derivative. Rather than debating the merits or weaknesses of fanfiction or making value judgments regarding its supposedly derivative nature, this project explores the role that influence — in particular, inter-authorial influence — plays in fanfiction production. Within literary communities where every work is already explicitly based upon one common world or group of characters, what does it mean when a fanfiction author cites a separate piece of fanfiction as inspiration for their work? Is this author’s work more similar to that one fanfic that inspired it than to every other fanfic centered around the same world or characters? My hypothesis is that fanfics that cite another fanfic as their inspiration will be more similar to their inspiration fanfic than to other fanfics within the same fandom in both in terms of the words used in the stories and in terms of the overall emotional arcs of the stories. This project explores whether fanfic pairings in which one work was cited as having inspired the other are more similar to each other than fanfic pairings within the same fandom but without any noted influence according to the metrics of cosine similarity and sentiment-arc variance.
A Dangerous Game: China's State Media Perceptions of Strategic Competition with the United States
Keywords: US-China Relations
Stanford East Asian Studies
Propaganda, Chinese
Great powers > Foreign relations
Text
Thesis

The perception of a “rising China” and “declining US” among Western academic and policymaking circles has sparked fears of an emboldened China that views military conflict as beneficial to their interests. This research aims to provide clarity on this issue by analyzing how Chinese state media portrays strategic competition with the US. Drawing from online Chinese-language news articles from media institutions that reflect the Chinese Communist Party’s official party line (People’s Daily, PLA Daily, Global Times, and Xinhua), I examine how these outlets depict economic, technological, military, political, and international competition with the US. This research analyzes these depictions against the backdrop of pivotal moments in 21st century US-China relations: the 2008 Great Recession, the 2012 Power Transition, the 2018 US-China Trade War, and 2020 COVID-19 Pandemic. Taking a random sample of articles from each time period, I assign each article a score ranging from “-1” to “1” to measure tone and calculate the percentage of positive, negative, and neutral stories within each time period and category of strategic competition. Second, I conduct a qualitative analysis of the articles within each random sample to identify general themes and messaging. This research identified a drastic rise in negative coverage of the US across the four time periods and five categories. This research also determined that Chinese state media has propagated a “peak-US” theory. According to this theory, the overall decline of the US has made it a more hostile power that aims to suppress China’s development and resurrect a “New Cold War.” Despite these views of a hostile US, state media narratives urged the US to return to cooperation and avoid war due to a recognition that China benefits more from overall cooperation with the US, especially in terms of economic and technological development.
A Living, Controllable Device:The Political Police and Informant Network in Socialist Hungary, 1956-1989
Keywords: Hungary
Socialism
Police > Political activity > History
Soviet Union
Revolution (Hungary : 1956)
Kádár, János, 1912-1989
Eastern Europe
Text
Thesis

After coming to power in the wake of the infamous Hungarian Revolution of 1956, János Kádár built his regime upon a delicate balance between upholding socialist ideals and appeasing a disgruntled, scarred nation. The slogan that became Kádár’s central doctrine perfectly represented this balance: “those who are not against us, are with us.” Existing literature on Kádárist Hungary portrays the period’s stability as a product of the leader’s compromising personality and political cunning. While accurate, these accounts largely neglect the role of Kádár’s political police as a primary organ of two-way interaction between the party and populace. Internal informational documents from the Ministry of the Interior III. reveal that the mass informant network served as the spearhead of interaction between the party and population in the realm of national security, which was viewed as essential to the building of socialism. The informant network’s transformation alongside the political police from a crude, coercive, and inherently dishonest body into a more focused, sophisticated, and interactive machine reveal much about the implementation of János Kádár’s vision for socialist Hungary. Described as a “living, controllable device,” the political police’s informant network provided Hungarian citizens with an accessible and personally beneficial means of cooperation with the state. The network allowed Hungarians to prove themselves “not against” Kádár’s Party.
A Mechatronic Solution for Time-Resolved Cryogenic Electron-Microscopy Sample Preparation
Keywords: Program in Engineering Physics
Time resolved cryo-EM
Text
Thesis

Cryogenic Electron Microscopy (Cryo-EM) is an established method of imaging biomolecules with an electron microscope. The current process of preparing samples for Cryo-EM involves loading the grid into a plunge-freezer and plunging it into liquid ethane to freeze the grid. As the grid falls into the liquid ethane, two samples are mixed and deposited on the grid using a microfluidic spraying device. In order to improve the practicality and repeatability of the plunge-freezer, a mechatronic device was built to simplify the process of replacing the microfluidic spraying device.
A Novel Application of Theoretical Models to Intimate Partner Violence Survivors’ Help-Seeking
Keywords: Medicine
Intimate partner violence
Help-seeking behavior
Theoretical models
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Thesis

Introduction: Intimate partner violence (IPV) survivors’ help-seeking is influenced by a complex interplay of barriers and facilitators. This includes both internal and external factors that affect a survivor’s decision to seek and ability to obtain resources. While understanding these factors is crucial to ensuring appropriate care, there is currently no agreed upon, comprehensive framework for capturing IPV survivors’ help-seeking.
Objectives: We seek to answer the question: How can the Three Delays Model (3DM) and the Behavioral Model of Health Services Use (BMHSU), as well as proposed adaptations of these models, provide a framework for understanding barriers and facilitators of IPV survivors’ help-seeking?
Methods: This secondary qualitative analysis was performed on transcripts from nine focus groups obtained as part of a larger study on IPV survivors’ help-seeking as influenced by the COVID-19 pandemic. Qualitative codes representing the theoretical constructs of each model and its adaptation were identified and set a priori, and to maintain a deductive coding approach, no new codes were added throughout data analysis. Manual coding of the transcripts was done by the first author. Once all transcripts were coded, code frequency and code co-occurrence analyses were performed to determine how well the different models mapped onto IPV survivors’ experiences.
Results: Codes from the BMHSU adaptation were applied most often, and codes from the 3DM adaptation were applied least often. The 3DM captured IPV survivors’ barriers to initially accessing care; however, it did not allow for a nuanced understanding of how the care system affected help-seeking. The BMHSU adaptation provided the richest understanding of IPV survivors’ barriers and facilitators to help-seeking with its expanded inclusion of psychosocial factors.
Conclusion: These findings highlight the strengths and limitations of two theoretical models and their adaptations, allowing for a better framework for understanding barriers and facilitators of IPV survivors’ help-seeking. Future research is needed to integrate these models into one comprehensive model for capturing factors contributing to IPV survivors’ help-seeking.
A Precinct-Level Analysis of Latino Voting Behavior During The 2016 And 2020 Presidential Elections
Keywords: precinct
Latino
voting behavior
ecological inference
Text
Thesis

The goal of this paper is to understand Latino voting behavior during the 2016 and 2020 presidential elections using precinct-level analysis. The 2016 and 2020 presidential elections were cases of elections where Latinos were demonstrated to have voted for Republican candidates in surprising numbers. Analysis of data from precincts with high proportions of Latino citizen voting age population was utilized to provide a comprehensive description of the Latino electorate and address the ecological inference problem that emerges when attempting to infer voting behavior of electorate subgroups like Latinos using aggregate data. Using precinct-level Latino demographic data created for this analysis, Latinos were found to have voted more Republican in 2020 than in 2016. Additionally, in 2020, Latinos were found to lean more Republican than non-Latinos in precincts with high proportions of Latino citizen voting age population.
A Predictive Model of Human Transcriptional Activators and Repressors
Keywords:  Department of Biology
Biology
Transcription factors
Genetic transcription
Text
Thesis

The ability to predict which protein sequences can act as transcriptional activators or repressors is important for understanding the function of human and viral transcription factors (TFs) inside human cells and for building synthetic biology tools for gene control. Here, I integrate multiple high-throughput data sets acquired using a recently developed method (HT-Recruit) that tests hundreds of thousands of protein sequences for their effect on reporter genes in live human cells. I first created a data processing pipeline using ground truth validations to regularize results from multiple HT-Recruit screens, allowing cross-screen comparisons as well as proper model training. After processing these datasets, I built and trained convolutional neural network machine learning models that predict both activation and repression for protein sequences across the human transcription factors. These are the first models to be trained on human TF data, as well as the first to predict repressors. Some protein sequences are bifunctional in that they both activate and repress, so it is important to be able to predict both.
A Proposed Framework of Manifesting 3D Forms from 2D Architectural AI Outputs
Keywords: Architecture
Artificial intelligence
Generative design
Text
Thesis

For much of recent history, the thought of machines being in any semblance within reach of human cognition has been one of imagination. But now, technology has speedily caught up to these aspirations, and society faces the daunting task of accepting, learning, and utilizing such tools. Specifically in the lens of design and architecture, what needs to be done at this point is to (1) reflect on how technology currently plays a role within it, and (2) determine how technology should continue to impact it.
In this thesis, I will survey artificial intelligence applications dedicated to image generation and explore how they serve the purposes of architectural design. In particular, I will utilize recently popular text-to-image applications, such as Midjourney alongside existing GANs. Given these current various outputs available from machine learning tools, there is yet no current framework or methodology defined for architects to utilize these tools to manifest a 3D structure out of them. I will propose a framework for how architects can use various generative architectural outputs, namely massing and programming, to realize an actual 3D structure that serves as inspiration in the early design stages. However, besides being a guideline for individuals who wish to use these tools, ultimately this framework could also be referenced as a preliminary model for those working on the continuous improvement of 3D machine learning algorithms. After all, as the design process inevitably becomes more digitized, it is important to maintain a humanistic and an authentically architectural approach within the algorithms that oversee our design outputs.
A Representative Role for the Alternative Splicing of Synaptic Genes
Keywords:  Department of Biology
alternative splicing
synapses
synapse adhesion molecules
neurexins
autism
Text
Thesis

Alternative splicing enables the differential expression of multiple mRNA transcripts and multiple functionally unique protein isoforms derived from the same gene. Interestingly, genes encoding synaptic regulators are both alternatively spliced and implicated in the development of several neuropsychiatric disorders including autism spectrum disorders, schizophrenia, and Tourette’s syndrome. Mechanisms by which synapses are formed and dynamically regulated remain unclear, but the alternative splicing of trans-synaptic regulators is thought to play a decisive role in mediating neuronal communication. To deduce a representative model for neuronal alternative splicing in the making and shaping of synapses (as reflected in changes to synaptic RNA and protein levels), Khdrbs (Sam68, Slm1, Slm2) and Nova splice factors (Nova1, Nova2) were virally overexpressed in primary neurons cultured from neonatal mice. In-vitro validation of RNA-seq-reported changes in synaptic splicing demonstrated that Khdrbs factors exclusively regulate the alternative splicing of multiple Neurexin (Nrxn) homologs, cell-adhesion molecules crucial for the development of functional synapses. Additionally, immunoblot analysis revealed a strikingly consistent loss of key synaptic proteins, coupled with decreased expression of astrocytic markers, in Slm1-overexpressed cultures, suggesting a splice factor-specific role in maintaining tripartite synapses by which glial contributions are likely paramount. In short, the experiments performed here capture the discrete effects of neuronal alternative splicing in the regulation of core synaptic components and offer insight into the molecular bases underpinning a broad range of animal behaviors.
A Socio-hydrological Framework to Assess Rate Design for Urban Water Affordability through Drought
Keywords: Rachunok, Benjamin
Thompson, Barton
Water resources
Droughts
Rate design
Socio-hydrology
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Thesis

Unaffordable water threatens water access in the United States, particularly for low-income households that struggle to pay the rising cost for water. In water-scarce cities, water shortages necessitate either expensive infrastructure development or costly emergency measures to meet demand, which in turn increase household water costs. Rate design plays a key role in determining whether these costs threaten water affordability for low-income households, but water utilities are often constrained by local and state policy in their ability to set progressive rates. Therefore, new approaches to design rates that optimize water affordability within the local legal and hydrological context are needed in drought-prone regions. To address this gap, we design a socio-hydrological modeling framework that fuses legal analysis, behavioral economics, and hydrologic modeling to assess the impacts of rate design on household water affordability. We demonstrate this framework in an illustrative application in Santa Cruz, California, where droughts threaten water supplies and California Proposition 218 deters public water utilities in setting progressive rate design. Initial results demonstrate that flat drought surcharges reduce affordability, particularly under increasing block rate tariffs. This framework can both help utilities design rates to improve water affordability in their socio-hydrological context and also illuminate the impacts of state policy on affordability outcomes.