VictorJuiz/thesis huggingface dataset:
In the mind of the beholder : neural and computational mechanisms underlying perceiver-driven biases in information processing

Rather than being passive observers, people can be better described as active perceivers of the world around them. Incoming information is not passively taken in, but is selectively filtered and altered by a perceiver's beliefs, goals and motivational states. The current work examines the neural and computational underpinnings of such perceiver-driven biases in information processing. In one study, we combined computational modeling and functional magnetic resonance imaging to provide a mechanistic account of motivational biases in perceptual judgments of ambiguous images. We showed that motivational biases can be dissociated into response and perceptual components, each associated with distinct neural processes. In a second study, we extended our work to a more naturalistic setting with dynamic audio-visual stimuli. We scanned participants watching videos about immigration policy and showed that political attitudes modulated neural responses to the videos. In particular, activity timecourses in the DMPFC was more similar between participants with similar political attitudes, than between participants with opposing political attitudes. Furthermore, the neural divergence between conservative-leaning and liberal-leaning participants was associated with increased use of moral-emotional language. Together, these studies explore the neural processes involved in biasing information processing, and provide illustrative examples of how the tools of cognitive neuroscience allow us to peer into the mind of the beholder.

Keywords: Psychology.
Advisor: Zaki Jamil
Gross James J. (Professor of psychology)
Poldrack Russell A.
In Word, Not Deed: The Politicization and Decentralization of the National HIV/AIDS Program in the Philippines

This year-long project explores the nuances of the Philippine National AIDS/STI Prevention and Control Program (NASPCP) within the context of a decentralized health system. The Philippines, unlike the rest of its Southeast Asian counterparts, has experienced a rapid increase of HIV cases within the last decade. This rising epidemic has mainly been isolated within marginalized and underserved populations such female sex workers (FSWs), males who have sex with males (MSMs), and people who use intravenous drugs (PWID). Nevertheless, the 140 percent increase in cases have also reflected a demographic shift to the young adult cohort, with a serious potential for the epidemic to spill over in the general population. In order to understand the gaps and barriers of the NASPCP in a different perspective, this study employed a mixed methods approach that involved qualitative research coding and a qualitative case study on three specific cities in Metro Manila with high prevalence of HIV/AIDS. This study incorporated the framework of decentralized local decision spaces to better understand the perspectives of those involved in HIV/AIDS care, research, and advocacy. Data analysis revealed the following main themes: (1) tension between national policy and local implementation; (2) local heterogeneity of HIV responses; (3) narrow decision spaces for regional and local public health stakeholders; and (4) interference of national and local politics on the proper flow of resources and information. Overall, the politicization of health and the extent at which the Philippine HIV healthcare system is truly devolved can constrain the comprehensive objectives of the NASPCP. HIV/AIDS is a serious public health and development problem for this developing nation. This study offers specific technical suggestions and research recommendations for those in the field to move forward. This study proposes that when working within HIV/AIDS program implementation and delivery of care, it is necessary to fix certain mechanisms of this decentralized system and seriously consider the role of politics in health.

Keywords:  Fisher Family Honors Program in Democracy
infectious disease
HIV/AIDS
HIV
decentralization
Philippines
health systems
human biology
CDDRL
Center for Democracy
Development
and Rule of Law
Thesis
Advisor: Eggleston Karen
Inclusive Innovation: Overcoming the Gender Gap in India's Emerging Mobile Health Ecosystem

Mobile health (mHealth) leverages the unprecedented reach of digital technologies to improve healthcare delivery. However, in comparison to Indian men, Indian women are 28% less likely to own cell phones and 58% less likely to use mobile internet. This thesis seeks to elucidate the implications of this gender gap for the Indian mHealth space, with a focus on how gendered innovation methodologies might contribute to greater gender equity. To do so, I begin by synthesizing currently disconnected literatures on mHealth, Information and Communication Technology for Development (ICT4D), and the role of gender in Indian society. The practical implications of gendered mobile phone dynamics are further expounded with a case study analyzing Kilkari, a maternal health audio-messaging mHealth program. This thesis then draws on semi-structured interviews with a range of actors in the Indian mHealth space to explore the potential relevance of gendered innovation methodologies. The discussion concludes with an analysis of systemic barriers to gender equity in Indian health care. If mHealth innovations are to truly benefit Indian women, the entire mHealth ecosystem must pursue a more inclusive approach to innovation.

Keywords: mobile phones
gendered innovation
mHealth
ICTs
ICT4D
inclusive innovation
India
STS
Thesis
Indigenous Fashion: Innovation through Enterprise

This Honors Thesis investigates the experiences of contemporary Native fashion entrepreneurs who are trailblazing Indigenous design and representation through innovative artistic and business practices. Through qualitative analysis of common themes shared between participatory entrepreneurs using the software NVivo, this work explores the opportunities for mentorship and industry expansion offered by their enterprises. Private enterprise is largely underdeveloped in Indian Country which motivated my exploration into the fashion industry. Given mentoring, support, and a vision, artists can and are finding innovative ways to expand this industry and promote compelling, authentic, respectful representations of Native nations. The participants in this study are inspirational examples of Indigenous artists who have begun their own enterprises by drawing on tradition and showcase the enduring and continuously evolving nature of Indigenous cultures. They center their businesses on core values of community, growth, respect, and advocacy. By proliferating their work, these entrepreneurs contribute to a growing industry of Native fashion which is actively combating appropriative practices. They are taking the means of production back into their own hands and have unlimited potential to empower themselves, other artists, and their communities.

Keywords: Indigenous
Native American
American Indian
Native American Studies
Center for Comparative Studies in Race and Ethnicity
Fashion
Entrepreneur
Designer
Artist
Tradition
Culture
Mentorship
Expansion
Contemporary
Thesis
Advisor: Snipp C. Matthew
Wilcox Michael
Biestman Karen
Inference of Sub-Resolution Stacking Patterns from Seismic Data in Spatially Coupled Models

Geological modeling and characterization of energy reservoirs greatly relies on the interpretation of seismic data due to its extensive spatial availability. Owing to many factors, including subsurface complexities, heterogeneity and limited seismic resolution, accurate description of subsurface geological configuration becomes a major challenge, giving rise to tremendous uncertainty in reservoir evaluation. The ability to reason about arrangements of thin sub-seismic scale beds is particularly important in the development of strongly heterogeneous unconventional formations due to complex completion strategies. This work is focused on quantitative interpretation of individual seismic traces and extraction of information relevant to recognition of various geological parasequences. The proposed workflow involves stochastic modeling of stratigraphic sequences comprising thin subresolution beds, and subsequent application of a generative statistical model to invert for those sequences from the respective acoustic responses.

Keywords:  Department of Energy Resources Engineering
School of Earth Energy &amp; Environmental Sciences
inverse problem
reservoir characterization
hidden markov model
Thesis
Advisor: Mukerji Tapan
Information processing : of humans, by humans and for humans

The core mission of information science is to design information processing tools that aid human communication and scientific discovery. Naturally, the development of such tools is shaped by concurrent understandings of human biology and behavior. In our 21st century world, myriad human-focused investigations have opened up rich potential markets for tool design. My research explores such intersections between human biology and information processing tool design, and the bidirectional influence between the two. In this dissertation, I detail three genres of information processing: of humans, by humans and for humans. For information processing of humans--that is, of humans' genomic sequencing data--I present two bioinformatics pipelines. To demonstrate information processing by humans, I describe a novel lossy image compression framework that is rooted in the human abilities to recognize, describe and generate images. Finally, I describe a science communication effort that employs computational tools to process information and produce journalistic media for human consumption. Together, these projects support the thesis of this dissertation: that information processing tools can be used to improve our understanding of human communication needs, and that an improved understanding of human communication can, in turn, be used to design better information processing tools.

Keywords: Electrical Engineering.
Advisor: Weissman Tsachy 
Gevaert Olivier Michel Simonne
Zou James
Information theoretic approaches to statistical learning problems

Machine learning algorithms have achieved super-human scores in several benchmark tasks from various areas such as computer vision, natural language processing, and speech processing. These algorithms, however, have been observed to lack sufficient robustness and generalizability necessary for their safe application to high-risk tasks such as designing self-driving cars and healthcare systems. A state-of-the-art learning algorithm demonstrates great generalization behavior over one particular domain of data, while the same algorithm can generalize poorly in other practical domains or under a slight domain shift such as adding minor additive noise to its input. Resolving such issues and improving robustness and generalization properties of learning algorithms require a thorough theoretical analysis of statistical learning settings, raising the need for novel theoretical approaches to analyze machine learning problems. This doctoral dissertation introduces several new theoretical frameworks for analyzing generalization and robustness in statistical learning settings. The proposed frameworks are inspired by existing tools and ideas from the following fields of study: information theory, convex analysis, Fourier analysis, and optimal transport theory. More specifically, this dissertation is divided into three parts. The first part focuses on developing information theoretic approaches to supervised learning problems. We consider and optimize well-known information measures such as entropy, mutual information, and maximal correlation to design robust supervised learning and feature selection algorithms. The second part discusses novel information theoretic frameworks for generative adversarial nets (GANs) and group-structured adversarial learning problems. We use convex analysis tools as well as optimal transport costs to develop those frameworks. The third part is dedicated to understanding generalization in various machine learning contexts such as classic kernel learning and modern adversarial deep learning. We introduce novel Fourier-based and spectral approaches to better understand generalization in different learning contexts and propose several regularization tools for improving the generalization performance in those learning settings.

Keywords: Electrical Engineering.
Advisor: Tse David 
Boyd Stephen P. 
Montanari Andrea 
Weissman Tsachy
Infrared laser absorption spectroscopy of nitric oxide for sensing in high-enthalpy air

Motivated by thermometry in high-enthalpy air, advancements towards the measurement and modeling of high-pressure laser absorption spectroscopy (LAS) of nitric oxide (NO) are presented. The primary application of this thermometer is to characterize the stagnation conditions (T = 1000--2500 K and P = 10--130 atm) in a clean-air hypersonic wind tunnel facility. By characterizing the thermodynamic conditions upstream of the expansion nozzle, the flow conditions of the expanding air can be determined via enthalpy matching. At high temperatures, the Zeldovich mechanism describes increasing NO formation in air with increasing temperature, making NO an attractive species for LAS-based temperature measurements in air. Two optical transitions in the R-branch of the fundamental rovibrational band of NO are selected and their fundamental spectroscopic parameters are characterized at high temperatures. The temperature sensor design is demonstrated in reflected shock wave experiments in a large diameter shock tube at pressures up to 5 atm. Although the target application's operating pressure range is well outside the demonstration range, the fundamental concept of two-wavelength absorption is still valid. However, at high pressures, the selected optical transitions begin to blend with their neighboring transitions. Thus, accurate knowledge of the high-temperature and high-pressure absorption at the selected wavelengths requires knowledge of the spectroscopic parameters defining the neighboring transitions. To measure the spectroscopic parameters of the many neighboring transitions, a high-pressure, high-temperature (HPHT) optical cell (up to 800 K and over 30 atm) is designed and demonstrated for mid-infrared spectroscopy with usable transmission up to approximately 8 microns. With a functional HPHT optical cell, a detailed, temperature-dependent study (up to 800 K) of the optical transitions in the NO R-branch near 5.3 microns is performed. To extend the study to temperatures relevant for the target sensing application, shock tube measurements from 1000 to 2500 K supplement the detailed study. Finally, the spectrum is studied at high pressures. Static cell measurements reveal deviations from the classical line shape models used accurately at low pressures. The deviations are attributed to collisional line mixing that emerges when the line widths of the optical transitions are of similar or greater magnitude than the separation of optical transitions. A temperature-dependent line mixing model is built using statistically-based energy gap fitting laws and the full relaxation matrix expression. A comparison with measured data reveals good agreement in the regions where inter-branch coupling can be neglected. In the end, a thorough treatment of the NO spectrum has provided a temperature- and pressure-dependent model that can be used to predict the absorption spectra of NO in the R-branch of the fundamental rovibrational band.

Keywords: Mechanical Engineering.
Advisor: Hanson Ronald 
Cappelli Mark A. (Mark Antony) 
Strand Christopher Lyle
Inhibiting the BMP pathway rescues neural stem cell defects in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disease observed with aging that represents the most common form of dementia. To date, therapies targeting later phenotypes including removing the plaques, tangles, or inflammation have limited efficacy. In a mouse model of AD harboring mutant amyloid precursor protein, we demonstrate that targeting an earlier phenotype, specifically impaired neural precursor cell (NPC) self-renewal, can reverse cognitive defects in AD. For the first time, we show this neural stem cell defect is cell intrinsic and occurs before the widespread inflammation and amyloid plaque pathology seen in AD, which may make it the earliest indicator of disease. To further query the mechanism of this self-renewal defect, we turn to single-cell RNA-sequencing of these neural precursor cell populations to show AD-enriched pathways that would be missed in bulk whole-brain RNA sequencing. Our analysis revealed BMP signaling as the only enriched pathway at this early phase that is consistently enriched with aging throughout the disease process. Genetic reduction of USP16, a critical physiological antagonist of self-renewal gene Bmi1, led to normalized BMP signaling, improved NPC self-renewal, and decreased Cdkn2a expression. While exploring later disease manifestations in AD, we found that the reduction in USP16 expression was sufficient to decrease astrogliosis in vivo and improve cognitive function. Hence, our data reveal USP16 as a novel therapeutic target that rescues neural precursor cell function in AD models prior to the onset of downstream pathology. This rescue occurs through repression of Cdkn2a and BMP signaling and may present a novel alternative therapy to drugs targeting plaques or inflammation.

Keywords: Stem Cell Biology and Regenerative Medicine.
Advisor: Clarke Michael F.
Heller H. Craig
Palmer Theo
Injecting Transparency into the AI Revolution: Mandated Documentation Demonstrating Results to Third Parties

The artificial intelligence (AI) revolution has brought about positive change, but it has also uncovered major issues with algorithmic bias and error – as seen in discriminatory predictive algorithms in the criminal justice system. Many call for transparency to solve these issues, but neural networks (NNs) – a prominent form of AI – are impervious to most traditional transparency approaches. Thus, this thesis investigates how society can compel developers to incorporate transparency into ML systems during the development process. Transparency is defined in this thesis as developers’ ability to justify system outcomes on sensitive groups by explaining decisions made during the development process. To do so, this thesis analyzes two forms of documentation proposed to inject transparency into algorithmic systems: 1) data protection impact assessments (DPIAs) within the EU’s General Data Protection Regulation and 2) Google’s model cards. This analysis is supplemented by interviews of six experts. From this analysis, this thesis surfaces four main challenges encountered by documentation pushing for transparency: 1) dangers from direct transparency to the public, 2) implementation difficulties, 3) tensions with other norms (especially privacy), and 4) a lack of positive incentives. This thesis then recommends a framework to address these challenges. In this framework, legally mandated forms of documentation requirements accompanied by standardized measurement of results will be the starting point for developers to incorporate transparency into ML systems. However, this formal documentation must be accompanied by efforts to establish broader norms that will emphasize the importance of incorporating transparency throughout the ML development process. These conclusions strongly suggest that policy-makers should enact legislation requiring such documentation. Effectively adopting this work will help address algorithmic error and bias issues plaguing AI as well as help push the AI revolution to the next level.

Keywords: artificial intelligence
machine learning
AI
ML
algorithmic bias
algorithmic harm
transparency
documentation
Science Technology and Society
Humanities and Sciences
Thesis
Advisor: Edwards Paul
Christin Angele
Input-output model of pulsed quantum optics

Pulsed quantum optical systems are a promising platform for quantum-enhanced information processing. This is fundamentally due to the presence of large optical bandwidth in such systems, which greatly enhances the optical nonlinearities while also giving rise to a multimode bosonic system with a high-dimensional Hilbert space. To study both of these implications quantitatively, we explore whether the dynamics of pulsed quantum optical systems can be effectively modeled with an input-output model - which are models of open quantum systems that interact with a Markovian bosonic bath. The explicit emphasis on input-output theory is motivated by the availability of theoretical and numerical tools which have been developed by the quantum optics community for analysis and simulation. We study ultrashort-pulse optical parametric oscillators (OPOs) and conclude that there are specific time-scale requirements for pulsed quantum optical systems to admit an input-output description. By performing numerical simulations of the input-output model, we find that the pulsed dynamics do lead to an enhancement of the optical nonlinearity, but the multimode pulsed nature of the pump can also act as decoherence channels. In addition to the work on pulsed quantum optics, this thesis also explores the applications of analytical and numerical tools of input-output theory to model and simulate quantum networks of optical components that are experimentally studied in our group. In particular, we derive quantum models for both the coherent Ising machine and the nonlinear coherent feedback control of a non-degenerate OPO with quantum network theory. Finally, this work also investigates the use of the quantum manifold formalism to reduce and simplify numerical models of multiatom cavity QED systems.

Keywords: Applied Physics.
Advisor: Mabuchi Hideo 
Fan Shanhui
Safavi-Naeini Amir H.
Inspireo: empowering students with digital agency in the attention economy

Have you ever been distracted by technology? You are not alone. Reports on student surveys show that digital distractions are interfering with their homework, personal relationships, and sleep. Though the amount of digital content has increased exponentially during the last decade, human cognitive control has not advanced at the same rate. The growing pervasiveness of technology has exposed students to an increasing number of challenges, such as keeping on-task, avoiding distraction, monitoring attention and managing goals. This effect has grown exponentially due to the rise of advertisement as a dominant business model in the technology companies most present in students' lives (e.g. Google and Facebook). Consequently, individual study time is increasingly ineffective because students are vulnerable to negative external distractions, but most importantly because technology reinforces beliefs of lack of ability to develop self-control. Our project explores the disempowering effect of technology on students’ sense of agency, and the vulnerability of students from underprivileged environments to overcome digital distractions. Through design-based research we created Inspireo, a mobile companion on Android to enhance learners’ beliefs in their capabilities to think and behave towards their goals, and their ability to self-regulate in an increasingly persuasive digital world. The pilot results on users after using inspireo show significant and large effects of improvement in self-efficacy on self-regulated learning, triangulated with self-reported qualitative data. Furthermore, we analyze equity gaps by socio-economic status, formulate a model for technologies that level the field of learning opportunities, and advocate for the development of technologies that *actually* empower students.

Keywords: inspireo
distraction
edtech
cognitive control
agency
digital
learning
design and technology
Thesis