VictorJuiz/thesis huggingface dataset:
Information theoretic approaches to statistical learning problems
Keywords: Electrical Engineering.

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.
Infrared laser absorption spectroscopy of nitric oxide for sensing in high-enthalpy air
Keywords: Mechanical Engineering.

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.
Inhibiting the BMP pathway rescues neural stem cell defects in Alzheimer's disease
Keywords: Stem Cell Biology and Regenerative Medicine.

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.
Injecting Transparency into the AI Revolution: Mandated Documentation Demonstrating Results to Third Parties
Keywords: artificial intelligence
machine learning
AI
ML
algorithmic bias
algorithmic harm
transparency
documentation
Science Technology and Society
Humanities and Sciences
Thesis

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.
Input-output model of pulsed quantum optics
Keywords: Applied Physics.

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.
Inspireo: empowering students with digital agency in the attention economy
Keywords: inspireo
distraction
edtech
cognitive control
agency
digital
learning
design and technology
Thesis

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.
Insurgency formation and civil war onset
Keywords: Political Science.

Why do civil wars begin? Since the end of the Cold War, civil conflict has become the most prevalent formed of armed conflict, but too little is known about the causes and evolution of these conflicts. Extant scholarship focuses on country-level conditions to explain variation in conflict onset, but this often misses pre-war mobilization and within-state variation in armed group campaigns. To address these gaps, I develop an original dataset on 1,582 armed groups operating around the world between 1970 and 2012. I collect new data on organizational characteristics and the timing of three distinct campaign stages. The dataset establishes several new sources of variation in the organization and evolution of armed groups to guide future research. I argue that existing explanations for civil war are relatively time-invariant and so poorly explain the timing of different campaign stages. As a result, I argue scholars must consider the strategic interactions between the state and an armed group to better understand the causes and evolution of these conflicts. I develop and test two separate explanations for rebel group formation and civil war onset using a combination of fieldwork interviews, supervised learning algorithms, and regression analysis. The first theory focuses on how poor economic conditions and ongoing conflict affect the probability of rebel group formation by lowering opportunity costs and shifting the state's relative capacity to detect emerging threats. The second theory focuses on how states condition their responses to emerging threats based and under what conditions miscalculations are more likely to lead to civil war. The results advance theoretical understanding about why civil wars erupt and provide a new dataset on armed groups for future terrorism and insurgency research.
Integrated motion planning and control for automated vehicles up to the limits of handling
Keywords: Mechanical Engineering.

In order to keep self-driving cars safe, it is important that these vehicles can plan safe trajectories through their environment and have the ability to robustly use their full tire-force potential. Racing at the limits of handling provides a purposefully challenging scenario for the development of reliable vehicle-motion planning and control techniques, as race cars are constantly pushed to their physical limits. With a common trajectory-tracking architecture for automated vehicle control, steering provides path-tracking control, and the throttle and brakes are used to track a desired speed profile. For the specific application of racing, this speed profile can be designed to fully use the tire-force potential. Experimental data show that a preexisting control framework based on this approach can match the lap time of an amateur race-car driver, but a professional race-car driver proves to be slightly faster. It is demonstrated with both experimental results and an analytical method that with this decoupled path-tracking and speed-tracking controller, an automated vehicle is prone to either under-utilize the tires or lose control over the path-tracking dynamics when unintentionally operating beyond the limit. Furthermore, a professional race-car driver successfully operates the vehicle with a control strategy that seems fundamentally different from trajectory tracking. Namely, he shows significant lap-to-lap variations in both speed and path, but he is consistently faster than automated trajectory-tracking control. This inspires new strategies for automated vehicle control. In this context, two novel feedback-control strategies are presented, which employ slip-angle control to robustly use the vehicle's full tire-force potential, while speed control provides the path-tracking functionality. Subsequently, in order to have the ability to also adjust the vehicle's path, a Nonlinear Model Predictive Control (NMPC) framework is presented which can trade-off longitudinal and lateral control inputs. Experimental results demonstrate that this controller successfully coordinates the inputs at the limits of handling. However, the computational burden of this NMPC framework limits the length of the planning horizon for real-time control, which in turn inhibits its ability to adjust the vehicle's path and speed. To address this issue, a new NMPC framework is developed, which serially cascades vehicle models of different levels of complexity in the planning horizon. Experimental results on an automated race car demonstrate the benefits of this new concept, with a high quality of control provided by a high-fidelity vehicle model in the near-term planning horizon, and significant extension of the planning horizon with a low-fidelity model.
Interactive simulation and design of yarn-level cloth patterns
Keywords: Electrical Engineering.

Modern design tools for yarn-level cloth patterns struggle to predict the final shape of knit or woven fabrics. My work describes an interactive design tool for authoring, simulating, and adjusting yarn-level patterns for knit and woven cloth. To achieve interactive performance for notoriously slow yarn-level simulations, I use two acceleration schemes: (a) yarn-level periodic boundary conditions that enable the simulation of only small periodic patches, thereby exploiting the spatial repetition of many cloth patterns in cardinal directions, and (b) a highly parallel GPU solver for efficient yarn-level simulation of the small patch. My system supports interactive pattern editing and simulation, and runtime modification of parameters. To adjust the amount of material used (yarn take-up) I support "on the fly" modification of (a) local yarn rest-length adjustments for pattern specific edits, e.g., to tighten slip stitches, and (b) global yarn length by way of a novel yarn-radius similarity transformation. I demonstrate the tool's ability to support interactive modeling, by novice users, of a wide variety of yarn-level knit and woven patterns. To validate this approach, I compare dozens of generated patterns against reference images of actual woven or knitted cloth samples, and I released this corpus of digital patterns and simulated models as a public dataset to support future comparisons. This work has served as the foundation for two other collaborative projects, including improving yarn-level cloth renderings via macro-fibers simulations, and a design tool for 3D woven materials. Finally, to avoid regenerating yarn curves for each pattern edit, I provide a method for live pattern updates in order to support interactive design of complex fabrics.
Interface circuits for affinity-free, label-free molecular detection platform
Keywords: Electrical Engineering.

The field of molecular diagnostics is dominated by affinity-based techniques (e.g., ELISA) where the critical signal processing steps are implemented in the chemical domain. This makes affinity-based techniques time consuming, expensive, and difficult to execute reliably outside of a laboratory environment. Affinity-free techniques address these problems by implementing the critical signal processing steps in the digital domain, where computation is more flexible and efficient. In this thesis we describe the development and implementation of electronic instrumentation for an affinity-free molecular sensing platform that records the vibrational signatures of proteins in blood using a nanoscale electrochemical interface. The sensing mechanism relies on coherent interference of electron wave functions at the interface between a nanoscale working electrode and a liquid electrolyte. Coherence at the sensing interface is enabled by low-noise potentiostatic feedback, which reduces the effective temperature of the electrons.
Interplay between mechanotransduction and force generation underlies cancer cell division in three dimensionally confining viscoelastic microenvironments
Keywords: Mechanical Engineering.

In physiological tissues, such as growing tumor spheroids, cells are surrounded by adjacent cells and viscoelastic extracellular matrices (ECMs). This confining microenvironment can mechanically constrain the growth of cells and the morphological changes required for mitosis. However, little is known about how the viscoelastic properties of ECM influence cell cycle progression, and how cells overcome the mechanical constraints to undergo mitosis. Here we probed the viscoelasticity of ECM and its impact on cancer cell division. First, we investigated the viscoelastic and viscoplastic properties of collagen gels, a primary component in ECM. Gels of collagen were found to exhibit remarkable viscoelastic properties; at greater deformations, they become stiffer but then flow more rapidly to relax this increase in stiffness, a behavior named strain-enhanced stress relaxion. We found that this behavior arises from force-dependent unbinding of weak bonds between collagen fibers. Collagen gels also exhibit mechanical plasticity, a property that leads to permanent deformation in response to a mechanical stress. The plastic properties of collagen gels allow cells to mechanical remodel the collagen. Next, we examined cell division in confining viscoelastic hydrogels. In confining hydrogels, cells are able to sense when growth is sufficient for division to proceed via a growth responsive TRPV4-PI3K/Akt-p27Kip1 signaling axis. When cells do progress to mitosis in the confining hydrogels, they are able to generate substantial protrusive forces that deform the microenvironment along the mitotic axis, clearing space for mitotic elongation to proceed. These forces result from interpolar spindle elongation and lateral contraction of the actomyosin ring, which drives expansion along the mitotic axis through volume conservation. Finally, we developed a new materials approach to tuning viscoelasticity of alginate-PEG hydrogels. Together, these findings shed light on the mechanical interactions between cells and their confining viscoelastic microenvironments during cell division.