VictorJuiz/thesis huggingface dataset:
Feedforward Learning Control For Multi Actuator Hard Drives And Freeform 3D Printers

This work addresses feedforward disturbance problems and trajectory tracking problems for both small-scale linear systems and large-scale nonlinear systems. The feedforward disturbance rejection problem, acoustic control problem, and trajectory tracking problem can all be formulated in a united manner as an optimization problem to minimize the difference between the plant output, under time-varying disturbances, and the desired reference response./nFor a single-input-single-output (SISO) linear system, the compensator or the tracking controller can be represented by an FIR filter or IIR filter. When statistics of the reference signals are unknown, an adaptive filter is used to find the optimal controller parameters based on some recursive algorithms. The least mean squares (LMS) algorithm and recursive least squares (RLS) have been widely used for feedforward adaptive control. However, when the reference is a sequence of impulses or wavelets, these algorithms may converge slowly or even diverge./nIn the first part of this work, a novel iterative batch least squares (IBLS) learning algorithm is developed for adaptive filtering with the reference consisting of a sequence of impulses or wavelets. The algorithm is formulated as a stochastic Newton optimization method with batch processing. The IBLS algorithm has been applied to a multi-actuator hard disk drive (HDD) to attenuate the vibration generated by the seeking actuator./nFor a large-scale multi-input-multi-output (MIMO) nonlinear system, the track following problem could be hard to solve. Recently, significant progress has been made in deep reinforcement learning that provides the flexibility to solve complex tasks from high-dimensional sensory inputs without knowing the dynamics of the environment. To do so, deep neural networks were used to approximate the action-value function or Q-function./nIn the second part of this thesis, we developed a modified deep deterministic policy gradient (DDPG) algorithm to address the trajectory following problem in a large-scale system with unknown dynamics. In this method, the reward function is defined as a function of the system state and its reference, which is maximized as long as the state of the system follows the desired trajectory. The modified DDPG algorithm is applied to a freeform 3D printing system to neutralize the effect of gravity and build a filament with the desired shape.

Advisor: Horowitz, Roberto
When microglia misbehave: exploring the consequences of Pogz disruption

Microglia are essential for brain development and homeostasis. Increasing evidence suggests that dysregulated microglia function contributes to neurodevelopmental conditions (NDCs), including autism. One of the most frequently mutated genes identified in NDCs, POGZ, encodes a putative epigenetic regulator and is highly expressed in hematopoietic lineage cells, from which microglia develop. POGZ expression in both the human and mouse brain peaks during prenatal development and global disruption of Pogz in mice elicits behavioral phenotypes relevant to autism. However, the role of Pogz in microglia is not known. Here we use a reverse genetics approach to investigate the role of Pogz in microglia. By generating mice with heterozygous deletion for Pogz in Cx3cr1-expressing cells, which include microglia, we show that Pogz haploinsufficiency in brain myeloid cells is sufficient to induce increased social interaction and cognitive impairments in male, but not female mice. Interestingly, this unique behavioral signature is consistent with behavior phenotypes associated with the rare genetic disorder White-Sutton Syndrome (WHSUS) in humans, caused by de novo POGZ mutations and characterized by an overly friendly demeanor and intellectual disability. Additionally, using shRNA knockdown and CRISPR/Cas9-mediated knockout in vitro, we show that Pogz is required for microglial transcriptional inflammatory response programs, in a potentially signal dependent manner. Together, these results demonstrate a novel, essential and likely sex dependent role for Pogz in microglia-mediated regulation of brain homeostasis and behavior. Thus, we suggest that the behavioral alterations observed in WHSUS result from brain myeloid cell dysfunction.

Advisor: Saijo, Kaoru
Counting Points on Igusa Varieties of Hodge Type

The Langlands-Kottwitz method seeks to understand Shimura varieties in terms of automorphic forms by deriving a trace formula for the cohomology of Shimura varieties which can be compared to the automorphic trace formula. This method was pioneered by Langlands [Lan77, Lan79], and developed further by Kottwitz in [Kot90, Kot92] in the case of Shimura varieties of PEL type with good reduction./nIgusa varieties were introduced in their modern form by Harris-Taylor [HT01] in the course of studying the bad reduction of certain simple Shimura varieties. The relation between Igusa varieties and Shimura varieties was expanded to PEL type by Mantovan [Man04, Man05], and their study of the cohomology of Igusa varieties was expanded to PEL type and streamlined in the Langlands-Kottwitz style by Shin [Shi09, Shi10]./nFollowing the generalization of Mantovan's work to Hodge type by Hamacher and Hamacher-Kim [Ham19, HK19], we carry out the Langlands-Kottwitz method for Igusa varieties of Hodge type, generalizing the work of [Shi09]. That is, we derive a trace formula for the cohomology of Igusa varieties suitable for eventual comparison with the automorphic trace formula./nIn order to carry out this method, we also formulate and prove an analogue of the Langlands-Rapoport conjecture for Igusa varieties of Hodge type, building off work of Kisin [Kis17] for Shimura varieties of Hodge and abelian type. Our subsequent arguments on the way to our trace formula are inspired by the techniques of Kisin-Shin-Zhu [KSZ21] using the Langlands-Rapoport conjecture to develop a trace formula for Shimura varieties of Hodge and abelian type.

Advisor: Shin, Sug Woo
An Auditory Memory System for Individual Vocal Recognition in the Zebra Finch

The zebra finch is a social songbird that lives in large groups and produces vocal communicationcalls to facilitate social interactions. A subset of these calls can be used for individual recognition, via distinct acoustic features that are stereotyped within a bird but individualized across birds. Given their large natural group sizes and the ethological importance of individual recognition, one might expect that zebra finches would have the capacity to recognize the calls of a large number of conspecifics. In this thesis, I describe a set of neuroethological experiments to test the memory of zebra finches for individual conspecifics by their vocalizations. We hypothesized that the caudal nidopallium (NCM), a higher-order auditory region of the avian brain analogous to mammalian auditory association cortex, is involved in the learning and retention of these auditory memories. Using an operant task in which birds were trained to associate the calls of some individuals with food reward, zebra finches were found to have a large capacity for recognizing individuals by their calls and song, and that those associations could be learned with just a few training examples and persist for at least a month without reinforcement. Furthermore, lesions to NCM eliminated previously formed associations but did not prevent re-learning or learning of novel stimuli, in contrast with lesions to vocal pre-motor pathways which had no effect on the recognition ability of the birds. Finally, using the spiking activity from single neurons across the cortical-like auditory regions of the brain, we found that familiar and task-relevant vocalizations elicited more reliable neural responses, with higher information capacity, than in response to unfamiliar and less behaviorally relevant calls.

Advisor: Theunissen, Frederic E
Word-medial syllabification and gestural coordination

The focus of this study is to use Electromagnetic Articulography (EMA) data to investigate the difference between word-initial and word-medial coordination, how coordination is affected by segment quality and stress, and to model coordination as it relates to syllabification. There are four key results of this study. First, segments in word-medial position have looser constrictions than segments in word-initial position, demonstrating that articulatory pressure is a driving factor in word-medial lenition. Second, word-medial syllables differ in coordination from word-initial sequences; however, current methods of analysis do not provide adequate tools to analyze such variability. Third, jaw oscillation does provide a metric for analyzing variability, crucially, demonstrating that word-medial syllables are more variable than initial syllables and unstressed medial syllables are more variable than stressed medial syllables. In addition, clusters coordinated within the same jaw phase show an increased likelihood to show c-centering and rightward shift. Thus, there is a gradient likelihood for medial clusters to syllabify according the Onset Maximization Principle, where initial stressed syllables are most likely and medial unstressed syllables are least likely, with the cluster syllabifying instead as a coda and onset. Fourth, coordination stability is influenced by by both coupling to jaw phase and intrinsic coordination between segments; both homorganic clusters and heterorganic clusters show coordinative stability, showing that jaw rather than gestural overlap between articulators determines stability; however, cluster composition and vowel quality interact and influence patterns of coordination such that a cluster is more likely to be syllabified as an onset when the cluster and vowel match in backness. Together, these findings demonstrate that word-medial coordination is dynamic and influenced by both top-down pressures, like stress, and bottom-up pressures, like segment quality. Furthermore, these findings demonstrate that jaw oscillation can be used as a diagnostic for motor planning and syllabification, providing a tool to improve existing models of planning and syllabification.

Advisor: Johnson, Keith
Inkelas, Sharon
Catherine the Great and the Development of a Modern Russian Sovereignty, 1762-1796

Historians of Russian monarchy have avoided the concept of sovereignty, choosing instead to describe how monarchs sought power, authority, or legitimacy. This dissertation, which centers on Catherine the Great, the empress of Russia between 1762 and 1796, takes on the concept of sovereignty as the exercise of supreme and untrammeled power, considered legitimate, and shows why sovereignty was itself the major desideratum. Sovereignty expressed parity with Western rulers, but it would allow Russian monarchs to bring order to their vast domain and to meaningfully govern the lives of their multitudinous subjects. This dissertation argues that Catherine the Great was a crucial figure in this process. Perceiving the confusion and disorder in how her predecessors exercised power, she recognized that sovereignty required both strong and consistent procedures as well as substantial collaboration with the broadest possible number of stakeholders. This was a modern conception of sovereignty, designed to regulate the swelling mechanisms of the Russian state. Catherine established her system through careful management of both her own activities and the institutions and servitors that she saw as integral to the system. She used a variety of management strategies that included imposing laws, issuing instructions, and routinizing interactions with administrators. While Catherine’s system, which was noble oriented, had limited purchase in the vast Russian countryside, it established clear expectations about the legitimate exercise of power. After her death, the system would not tolerate lightly any violation of its principles. Catherine’s successors, Alexander I and Nicholas I, largely adhered to these arrangements, allowing the Russian monarchy to remain a robust and viable form of government in the first half of the nineteenth century.

Advisor: Frede-Montemayor, Victoria
Towards a Theory of Subsegmental and Subfeatural Representations: The Phonology and Typology of Nasality

This study addresses one of the most fundamental questions in formal phonology, namely What are the units of phonological representation that grammars manipulate? Linguists have long assumed that segments and binary features are the basic atoms of phonological representations. The present study challenges this assumption by proposing that subsegmental units can be defined with respect to two distinct dimensions of representation: (i) the temporal dimension; and (ii) the spatial dimension, roughly equivalent to the physical magnitude of an articulatory gesture. I draw on two case studies providing instrumental data on two endangered Amazonian languages of Brazil, Panãra [kre] and Kawaiwete [kyz]. Using oral and nasal airflow data, I show that, on the one hand, Panãra exhibits a surface contrast between prenasalized oral stops [nt] and postoralized nasal stops [nt], which crucially differ in the extent of the duration of nasal airflow. On the other hand, Kawaiwete exhibits a distinction between fully oral, partially nasal, and fully nasal vowels, which crucially differ with respect to their degree of opening of the velo-pharyngeal port./nThe proposed representational model integrates the basic architecture of Q Theory (Inkelas & Shih 2016; 2017; Shih & Inkelas 2014; 2019) and subfeatural representations (Lionnet 2017) into a single unified framework. On the basis of the Panãra data, I argue for Q theoretic subsegmental representations, which divide the segment into three quantized and linearly ordered subsegments, (q1 q2 q3). This architecture provides the level of granularity necessary to distinguish between post-oralized nasals and pre-nasalized obstruents, where the former is represented with two nasal subsegments followed by one oral subsegment, and the latter is represented with a single nasal subsegment followed by two oral subsegments. Building on these Q theoretic representations, I argue on the basis of Kawaiwete for a scalar decomposition of phonological features, where continuous values can be grouped into one of three possible subfeatural categories: [+F], [?F], and [–F]. In the case of the feature [nasal], I argue for three perceptibly distinct degrees of nasalization: fully nasal [+nasal], partially nasal [?nasal], and fully oral [–nasal]. I show that a model of phonological representations which makes use of both subsegments and subfeatures is not only able to account for the data from both Panãra and Kawaiwete, but can also be extended to account for the full typology of phonological processes involving local nasalization or oralization.

Advisor: Inkelas, Sharon
Michael, Lev
High-Performance Architectures for Vehicle Propulsion: An Unconventional Approach to Design, Fabrication and Analysis using Scalable Flying Capacitor Multilevel Converter Modules

The consequences of climate change grow more pronounced each year as carbon emissions continue unabated. Decarbonizing all aspects of the transportation sector is a major requirement for a sustainable pathway to mitigate these consequences. However, this requires overcoming substantial engineering challenges, not the least of which are the power density and efficiency requirements of the inverter system in future electric drivetrains. This work examines a high-performance and scalable approach for modeling, designing, fabricating and testing an architecture that meets aggressive industry targets through innovative techniques and the use of an unconventional topology. Several prominent and promising topologies are reviewed, and an unconventional approach using the flying-capacitor multilevel topology is introduced to drive the order-of-magnitude improvements required by the industry targets. A 10-level, 1 kV, dual-interleaved converter module serves as the platform for an in-depth study of the electrical and thermal design and performance attained using this approach, where a peak power of 18.9 kW and a peak efficiency of 98.95 % is experimentally demonstrated. Scalability and reliability of the proposed architecture is discussed and tested using individual modules as well as an array paralleling nine of these 38.4 kW/kg, 24.4 kW/L modules. Finally, potential extensions to the work in future studies are reviewed.

Advisor: Pilawa-Podgurski, Robert
Training, Evaluating, and Understanding Evolutionary Models for Protein Sequences

Novel protein sequences arise through mutation. These mutations may be deleterious, beneficial, or neutral; the effect of a mutation on an organism’s evolutionary fitness is reflected in whether an organism survives long enough for its proteins to be sampled and deposited in a sequence database. Bioinformatics has long sought to use this evolutionary signal, commonly in the form of Multiple Sequence Alignments (MSAs), to make inferences as to the structure and function of novel proteins. With the advent of neural networks and self-supervised pretraining, a different approach emerged, where a large scale neural network could be pretrained using a language modeling objective to automatically produce informative features from an input protein sequence./nIn this work, methods to train and evaluate protein language models on a common benchmark are introduced. Subsequently, the effects of increased model scaling, dataset preprocessing and training hyperparameters on the ability of transformers to learn protein contacts without supervision are explored. A novel method operating on MSAs instead of single sequences is then presented, and shown to achieve state-of-the-art performance on several downstream tasks. Finally, the utility of all of these methods in protein design is discussed.

Advisor: Abbeel, Pieter
Canny, John
Exploration and Safety in Deep Reinforcement Learning

Reinforcement learning (RL) agents need to explore their environments in order to learn optimal policies by trial and error. However, exploration is challenging when reward signals are sparse, or when safety is a critical concern and certain errors are unacceptable. In this thesis, we address these challenges in the deep reinforcement learning setting by modifying the underlying optimization problem that agents solve, incentivizing them to explore in safer or more-efficient ways./nIn the first part of this thesis, we develop methods for intrinsic motivation to make progress on problems where rewards are sparse or absent. Our first approach uses an intrinsic reward to incentivize agents to visit states considered surprising under a learned dynamics model, and we show that this technique performs favorably compared to naive exploration. Our second approach uses an objective based on variational inference to endow agents with multiple skills that are distinct from each other, without the use of task-specific rewards. We show that this approach, which we call variational option discovery, can be used to learn locomotion behaviors in simulated robot environments./nIn the second part of this thesis, we focus on problems in safe exploration. Building on a wide range of prior work on safe reinforcement learning, we propose to standardize constrained RL as the main formalism for safe exploration; we then proceed to develop algorithms and benchmarks for constrained RL. Our presentation of material tells a story in chronological order: we begin by presenting Constrained Policy Optimization (CPO), the first algorithm for constrained deep RL with guarantees of near-constraint satisfaction at each iteration. Next, we develop the Safety Gym benchmark, which allows us to find the limits of CPO and inspires us to press in a different direction. Finally, we develop PID Lagrangian methods, where we find that a small modification to the Lagrangian primal-dual gradient baseline approach results in significantly improved stability and robustness in solving constrained RL tasks in Safety Gym.

Advisor: Sastry, Shankar
Abbeel, Pieter
Beauty in the Struggle: Toxic Stress, Adverse Environments, and Collective Healing

Early life experience and environmental context “gets under our skin,” playing a role instress reactivity and affecting health over one’s life (Corburn, 2017; McEwen, 2012; Shonkoff et al., 2012). The body’s physiological response to strong, frequent, or prolonged adversity in the absence of supportive adult relationships during childhood development is toxic stress (Shonkoff et al., 2012). It can result in impaired development that impacts health and wellbeing over the life course (Burke Harris, 2018; Center on the Developing Child at Harvard University, n.d.; Purewal Boparai et al., 2018; Shonkoff et al., 2012). Increased likelihood of chronic conditions such as diabetes and hypertension, increased likelihood of mental health issues such as depression and anxiety, and the development of negative coping mechanisms are associated with toxic stress (Burke Harris, 2018; Center on the Developing Child at Harvard University, n.d.; Purewal Boparai et al., 2018; Shonkoff et al., 2012)./nA growing body of literature recognizes the need for greater exploration of toxic stressoutside of clinical and social service settings, a movement upstream to situate toxic stress research in an ecological context (Burke Harris, 2018; Corburn et al., 2014; Ellis & Dietz, 2017; Shonkoff et al., 2012). My dissertation research is part of this growing area of toxic stress research. In this dissertation, I have three aims: 1) to identify concepts and frameworks in the academic and professional literature that situate toxic stress within an ecological context, 2) to understand toxic stress and healing at the community level through a case study of a community-driven process as collective healing, and 3) to explore youth perspectives of and experiences with toxic stress and adversity. I will achieve these aims through the completion of a narrative literature review, case study, and youth Photovoice project.

Advisor: Corburn, Jason