VictorJuiz/thesis huggingface dataset:
Disruptive Research on Distributed Machine Learning Systems

Deep Neural Networks (DNNs) enable computers to excel across many different applications such as image classification, speech recognition and robotic control. To accelerate DNN training and serving, parallel computing is widely adopted. System efficiency is a big issue when scaling out. High communication overheads and limited on-device memory are two major causes for system inefficiency in distributed machine learning./nThis dissertation studies possible ways to mitigate communication bottlenecks and achieve better on-device memory utilization in data and model parallelism for distributed machine learning workloads./nOn the communication side, our Blink project mitigates communication bottleneck in data parallel training. By packing spanning trees rather than forming rings, Blink achieves higher flexibility in arbitrary networking environments and provides near-optimal network throughput. To eliminate the communication in model parallel training and inference, we go abovefrom system layer to application layer. Our sensAI project decouples a multi-task model into disconnected subnets, where each subnet is responsible for decision making of a single task or a subset of the original task-set./nTowards better utilization of on-device memory, our Wavelet project intentionally adds task launching latency to interleave peak memory usage across different waves of training tasks on the accelerators. By packing multiple training waves on the same accelerator, it improves both computation and on-device memory utilization.

Advisor: Stoica, Ion
Reduced-Order Cerebrovascular Modeling of the Aging Vascular System

In medical conditions with disrupted cerebrovascular flow, including chronic alterations throughout aging or acute events such as ischemic stroke, cerebrovascular modeling can provide mechanistic and treatment insights. Reduced-order cerebrovascular modeling can be particularly useful for patient-specific or population-averaged cerebrovascular hemodynamics, where the governing flow dynamics include physically-driven non-linear constraints with active homeostatic regulation as well as highly variable and interconnected cerebral blood vessel networks. A coupled one-dimensional lumped parameter network (1D-LPN) cerebrovascular model is described with myogenic and CO2 reactivity autoregulation modeling, circle of willis anatomical variations, cortical collateral flow compensation, cerebrospinal fluid generation/reabsorption, and intracranial pressure dynamics modeling. For parameter tuning of the numerous cerebrovascular model variables, a Bayesian optimization procedure establishes crucial parameter values with the remaining model parameters assigned through vascular aging and cerebral autoregulation quantitative population-averaged reviews. A specific focus on cerebrovascular aging alterations, a major source of vascular variance besides patient-specific variations, explores the parameter tuning procedure in a young population. The coupled 1D-LPN cerebrovascular model design, age-related vascular critical review, cerebral autoregulation critical review, and model parameter tuning procedure, comprises a prototype vascular test bed with the capability to model cerebrovascular-specific medical conditions while accounting for age-related vascular alterations.

Advisor: Shadden, Shawn C
Breast Cancer Survival in African American and White Women: An Assessment of Modifiable Factors

Background: Breast cancer is the leading cancer diagnosis and the second leading cause of cancer-related death in the United States. However, the overall mortality rate declined more than 42% over the last 30 years due to advances in treatment and screening. Despite these advancements, the breast cancer mortality rate for Black women is persistently higher than any other group with a 41% gap in survival compared to White women. The cause of this disparity is complex and partially attributed to non-modifiable factors such as tumor biology and modifiable factors such as access to quality breast cancer treatment, reductions in obesity, and other chronic conditions. The purpose of this research is to examine the contributions of body mass index, comorbidity, and hospital characteristics to the breast cancer mortality disparity between Black and White women./nMethods: This dissertation assesses the contributions of comorbidity, obesity, and hospital characteristics to breast cancer mortality disparities between Black and White women. Chapter 1 provides an overview of breast cancer mortality trends, a review of the literature on the associations of modifiable factors associated with breast cancer mortality and presents the questions and hypotheses for the proceeding chapters. Chapter 2 is a systematic review of peer-reviewed articles to evaluate comorbidity as a primary contributor to breast cancer mortality disparities between Black and White women. Chapter 3 presents the findings of the evaluation of body mass index as a modifier of breast cancer and all-cause mortality disparities between Black and White women using a historical breast cancer cohort dataset. Chapter 5 presents findings from our study of the associations between hospital characteristics, breast cancer surgical volume and hospital area-level poverty, and breast cancer mortality and all-cause mortality. The final chapter provides a summary of our findings and suggestions for future research./nSignificance: Identifying and addressing modifiable risk factors for breast cancer mortality is key to narrowing the breast cancer mortality gap between Black and White women. Obesity, comorbidity, and hospital quality are three such modifiable factors. This research aims to contribute to existing knowledge and elucidate the roles of obesity, comorbidity, and hospital characteristics to adverse breast cancer outcomes among Black women.

Advisor: Mujahid, Mahasin S
The Design and Implementation of User-Schedulable Languages

/nThis thesis details an emerging class of programming language designs, called user-schedulable languages, that provide a safe and productive performance engineering environment for modern, heterogeneous hardware. The defining trait of user-schedulable languages is the division of program specification into two key parts: the algorithm, which defines functionally what is to be computed, and the schedule, which defines how the computation should be carried out. Importantly, algorithms have semantics independent of any schedule, and schedules are semantics-preserving with respect to the algorithm. Thus, programmers are freed from a large class of bugs. Because algorithm languages tend to be functional and domain-specific, the scheduling language can be very expressive. Existing scheduling languages include many program transformations, from accelerator offloading to tricky tiling and interleaving strategies. Multiple schedules can be written for different sets of hardware targets and can be maintained independently from one another./nHere, we formally analyze the design of an existing and widely deployed user-schedulable language, Halide, and find and correct several serious bugs and design flaws through this analysis. We also detail both the design and implementation of a new user-schedulable language, Exo, whose design is informed by the lessons learned analyzing Halide. Unlike Halide, which models scheduling as a parameter to a monolithic lowering process, Exo uses a rewrite-based scheduling system. This system doubles as an instruction selection process for custom accelerator hardware; importantly, these instructions can be specified in user programs, rather than inside the compiler itself. We then discuss a novel, high-performance, reference-counting memory management strategy suitable for recursive programs with highly non-local control flow over a (co-)inductive data domain. Finally, practical considerations for language design are discussed; these are lessons learned from maintaining and deploying these systems in practice./n

Advisor: Ragan-Kelley, Jonathan
Ultrafast Carrier-Lattice Dynamics in Nanoparticles and Films using Extreme Ultraviolet Transient Absorption

The motion of electrons and nuclei, or of charge carriers and lattice, respectively, in a solid system, are the most fundamental dynamics in nature. Understanding the ultrafast behavior of electrons, holes and phonons is critical to understanding and controlling the light-induced response of materials, most crucially for photovoltaic and photoelectrochemical application. In these materials, it is not understood why nanoparticles and other nanoscale systems exhibit such different macroscopic responses to solar irradiation, such as increased photocurrent or improved charge transfer efficiency. Unfortunately, few techniques exist with which to measure photoexcited phenomena on the femtosecond and picosecond timescales that electronic and nuclear motion occurs, and even fewer are sensitive to both charge carriers and lattice changes. Here, the technique of extreme ultraviolet (XUV) transient absorption spectroscopy is applied to a variety of nanoparticle and nanoscale solar energy relevant material systems to uncover the coupled carrier-lattice relaxation processes following absorption of a solar photon. XUV light initiates a core-to-valence transition, so it is uniquely able to simultaneously measure electrons, holes, and lattice dynamics with few-femtosecond time resolution. XUV is also sensitive to oxidation state and spin state and is element specific, making it ideal for answering the lingering questions in the solar energy field./nIn this dissertation, we begin in Chapter 1 with a description of semiconductors and nanoparticles, focusing on the ultrafast dynamics of charge carriers and the lattice. Then, core-level spectroscopy is introduced, and the process of high-order harmonic generation to create the XUV core level spectrum is explored. In Chapter 2, the ultrafast laser and high vacuum setup is presented, followed by an analysis of nanoparticle thin film sample preparation. Chapter 3 delves into the first nanoparticle transient XUV absorption experiment, which utilizes iron oxide nanoparticles (goethite α-FeO(OH)) and bulk iron oxide films (hematite α-Fe2O3) to demonstrate a link between morphology, crystal structure and photoexcited small polaron formation. A nanoscale metal-oxide-semiconductor junction of Ni-TiO2-Si is explored in Chapter 4, and the ultrafast hole dynamics across all three layers of the junction are revealed. In Chapter 5, a hot phonon bottleneck is demonstrated to slow hot carrier cooling in silicon nanoparticles following above-gap photoexcitation when compared to a single-crystal silicon bulk. Finally, Chapter 6 outlines the current progress studying hot hole and lattice relaxation in the ferroelectric semiconducting halide perovskite CsGeI3 and proposes future directions in nanoparticles and thin films with extreme ultraviolet transient absorption spectroscopy.

Advisor: Leone, Stephen R
The Development of an Unconventional Qa1b Restricted T cell Population

CD8 T cells restricted to the nonclassical MHC 1 (MHC 1b) molecule MHC-E show great promise in vaccine settings, but the development and specificity of MHC-E restricted T cells remains poorly understood. Here we focus on a CD8 T cell population reactive to a self-peptide (FL9) bound to mouse MHC-E (Qa1b) that is presented in response to loss of the classical (MHC 1a) processing enzyme ERAAP, termed QFL T cells. We find that mature QFL thymocytes are predominantly CD8+CD4-, show signs of agonist selection, and give rise to both CD8 and CD8 intraepithelial lymphocytes (IEL), as well as circulating memory phenotype CD8 T cells. QFL T cells require the MHC 1 subunit -2 microgloblin (2m), but do not require Qa1b or epithelial MHC 1 for positive selection. However, QFL thymocytes do require Qa1b for agonist selection and full functionality. Our data highlight the relaxed requirements for positive selection of an MHC-E restricted T cell population, and suggest a CD8+CD4- pathway for development of CD8 IELs.

Advisor: Robey, Ellen A
Methylation-based methods for studying chromatin structure

DNA is decorated with chemical marks and proteins that allow our genome to encode countless distinct cellular states. Proteins in the nucleus interact with DNA and mediate methylation of cytosine bases, modifications to the histones around which DNA wraps, the degree of compaction of DNA, and the spatial localization of DNA within the nucleus. These epigenetic regulatory elements determine the binding of transcriptional machinery that control gene expression and give rise to cellular diversity. Measuring where proteins bind to the genome and how the epigenome regulates gene expression can reveal underlying mechanisms that control cell state and can help uncover how these mechanisms are modified in diseased states. In this dissertation, I describe a suite of tools that measure protein-DNA interactions by encoding these epigenetic features in exogenous methylation of genomic DNA and detecting these marks with DNA sequencing platforms./nFirst, I extend and optimize short-read sequencing techniques for measuring protein-DNA interactions with the goal of applying these methods to single cells. Single-cell measurements capture dynamics occurring in small populations of cells and can reveal coordinated features within a cell that cannot be measured with bulk methods. Short-read methods map features of chromatin structure through base conversion, fragmentation, or selective enrichment of short sequences of DNA and leverage high-throughput DNA sequencing to detect enrichment of these features genome-wide. Next, I detail a method that I developed collaboratively that leverages long-read sequencing to measure protein binding events. Long-read sequencing provides a new dimension in which to encode information about the chromatin structure of a cell because long-read sequencers can read out not only nucleotide bases, but also modifications to those bases. With this new encoding space, we take multi-omic measurements of endogenous DNA methylation directly together with other elements of chromatin structure (e.g., histone modifications, DNA accessibility, DNA spatial localization, and protein binding events) by encoding these elements as DNA base modifications.

Advisor: Streets, Aaron
Multi-task Policy Learning with Minimal Human Supervision

Multi-task policies enable a user to adjust their desired objective or task parameters withouthaving to train a new policy for every new desired task. In order to train multi-task policies that can generalize to unseen tasks it is common to train them on a large repository of tasks. Tasks are commonly learned with demonstrations or reward functions. However, collecting human demonstrations or instrumenting reward functions for each new task is expensive and limits scaling of multi-task policies. How tasks are specified to multi-task policies is also an important dimension that can result in expensive labor during task communication. In this thesis we explore ways to learn and specify new tasks with minimal human supervision to enable more scalable multi-task policies.

Advisor: Darrell, Trevor
Voicing Silent Speech

This thesis concerns the task of turning silently mouthed words into audible speech. By using sensors that measure electrical signals from muscle movement (electromyography or EMG), it is possible to capture articulatory information from the face and neck that pertains to speech. Using these signals, we aim to train a machine learning model to generate audio in the original speaker's voice that corresponds to words that were silently mouthed. We call this task voicing silent speech./nVoicing silent speech has a wide array of potential real-world applications. For example, it could be used to allow phone or video conversations where other people around the person speaking can't hear anything they say, or it could be useful in some clinical applications for people who can't speak normally but still have use of most of their speech articulators./nThere have been several papers in the past that have looked at the problem of converting EMG signals to speech. However, these prior EMG-to-speech works have focused on the artificial task of recovering audio from EMG that was recorded during normal vocalized speech. In this work, we will instead generate speech from recordings where no actual sound was produced. Models trained only on vocalized speech perform poorly when applied to silent speech due to signal differences between the two modes. Our work is the first to train a model on EMG from silent speech, allowing us to overcome these signal differences./nTraining with EMG from silent speech is more challenging than with EMG from vocalized speech, because when training on vocalized EMG data we have time-aligned speech targets but when training on silent EMG data there is no simultaneous audio. Our solution is to adopt a target-transfer approach, where audio output targets are transferred from vocalized recordings to silent recordings of the same utterances. To do this cross-modal training, we need to account for the fact that the two recordings are not time-aligned, so a core component of our work concerns finding the best way to align the vocalized speech targets with the silent utterances./nTo enable development on this task, we collect and release a dataset of nearly twenty hours of EMG speech recordings, nearly ten times larger than previous publicly available datasets. We then demonstrate a method for training a speech synthesis model on silent EMG and propose a range of other modeling improvements to make the synthesized outputs more intelligible. We validate our methods with both human and automatic metrics, demonstrating major improvements in intelligibility of generated outputs.

Advisor: Klein, Dan
Banning left turns the right ways: Studies of turn prohibitions and their interaction with congestion pricing

This dissertation aims to increase the understanding of supply- and demand-side congestion management strategies. In particular, the work focuses on left-turn bans and congestion pricing. It proves the existence of an optimal spatiotemporal zone within which to ban left turns during the morning rush in cities with central business districts. The optimal ban is capable of reducing vehicle hours travelled significantly. Contrary to popular assumptions, left-turn bans are also capable of reducing vehicle miles traveled. The effectiveness of turn prohibitions is impacted by the distribution of origins and destinations, route lengths and time of day. Moreover, this dissertation explores the effects of combining turn prohibitions with different types of congestion pricing strategies. For the case of Stockholm, simulations show that combining cordon-based congestion pricing with the optimal portion left-turn ban creates a win-win scenario. Either additional travel time benefits are gained at the original toll levels; or the original benefits are maintained at reduced toll levels. Combining the two measures also highlights the importance of optimizing left-turn bans since banning turns in wholesale fashion does not improve Stockholm’s travel conditions. Furthermore, combining turn prohibitions with distance-based congestion pricing for a case study of downtown LA creates synergistic benefits. The reductions in travel costs due to the joint deployment of both strategies is greater than the sum of the reduction due to each strategy on its own. Adding turn prohibitions to a priced network allows commuters to arrive to their destinations both faster and closer to their desired arrival time.

Advisor: Cassidy, Michael
Stability of the Fixated Retinal Image During Monocular and Binocular Tasks

The movements of the eye play an integral part in constructing a smooth perception of the world around us. The fact that our eyes are foveal, with a high resolution center and a radial decrease in resolution as we move away from this center, necessitates a constant shifting of our gaze around our environment. Although our eyes are in constant motion we do not perceive the world as a series of static snapshots and constant jitter, instead our visual system is able to effectively integrate information over time to render the world veridically; a complex array of color, depth, and motion. These eye movements are essential to vision for a wide variety of reasons. In fact, movement is such a key aspect of vision that if the projection of the world onto the retina is rendered perfectly still, our vision will slowly fade. Our brain relies on a constantly fluctuating signal in order to generate a visual percept, and without this temporal variation, vision is impossible. Even when fixating on a point, our eyes are still engaging in a series of small movements to examine and enhance the fine details of the foveated stimuli. Collectively, these small movements are referred to as fixational eye motion (FEM). In order to truly understand how these small temporal variations will contribute to vision, it is necessary to understand how these movements interact with the photoreceptors in the retina. To this end we utilized an Adaptive Optics Scanning Laser Ophthalmoscope to project stimuli onto the photoreceptor mosaic and simultaneously recover an unambiguous trace of the motion of these stimuli as the eye engages in fixational eye movements.

Advisor: Roorda, Austin
New Information Inequalities with Applications to Statistics

We introduce, under a parametric framework, a family of inequalities between mutual information and Fisher information. These inequalities are indexed by reference measures satisfying a log-Sobolev inequality (LSI), and reveal previously unknown connections between LSIs and statistical inequalities. One such connection is shown for the celebrated van Trees inequality by recovering under a Gaussian reference measure a stronger entropic inequality due to Efroimovich. We further present two new inequalities for log-concave priors that do not depend on the Fisher information of the prior and are applicable under certain scenarios where the van Trees inequality and Efroimovich’s inequality cannot be applied. We illustrate a procedure to establish lower bounds on risk under general loss functions, and apply it under several statistical settings, including the Generalized Linear Model and a general pairwise comparison framework.

Advisor: Courtade, Thomas