VictorJuiz/thesis huggingface dataset:
Hardware-Aware Efficient Deep Learning

Significant improvements in the accuracy of Neural Networks (NNs) have been observed for a wide range of problems, often achieved by highly over-parameterized models. Despite the accuracy of these state-of-the-art models, their sheer size makes it impossible to deploy them for many resource-constrained applications, such as real-time intelligent healthcare monitoring, autonomous driving, audio analysis, and speech recognition. This creates a problem in realizing pervasive deep learning, which requires real-time inference with low energy consumption, high accuracy, and limited computational resources./nAchieving efficient NNs that can achieve real-time constraints with optimal accuracy requires the co-optimization of 1) NN architecture design, 2) model compression methods, and 3) the design of hardware engines. Previous work pursuing efficient deep learning focused more on optimizing proxy metrics such as memory size and the FLOPs, while the hardware specifications actually play an important role in determining the overall performance. Furthermore, due to the extremely large design space, the aforementioned three aspects are often optimized separately and empirically inprevious literature, making the whole design process time-consuming and sub-optimal./nIn this dissertation, we first systematically studied the quantization method, which is a widely used and standard model compression technique. Instead of using a heuristic design or costly searching, we tackled the mixed-precision quantization problem by leveraging the Hessian information, and our proposed Hessian-AWare Quantization (HAWQ) method achieved state-of-the-art performance on different networks and datasets. We further made the whole pipeline fully automatic (HAWQV2) and explored different aspects of quantization (ZeroQ) on different tasks (QBERT)./nBased on our systematic quantization method, we then included hardware specifications and deployment into the design space (HAWQV3). The neural architecture was taken into the co-design (CoDeNet) and was searched automatically as well (HAO). Finally, we increased the efficiency of the whole automatic HW-SW co-design pipeline by introducing teacher-based block-wise distillation (ETA). Overall, our work in this dissertation demonstrates steps in the evolution from traditional NN design toward hardware-aware efficient deep learning. We believe this will further acceleratethe deployment of advanced NNs on resource-limited devices and in real-world applications.

Advisor: Keutzer, Kurt
Social Dynamics of Machine Learning for Decision Making

From education to hiring, consequential decisions in society increasingly rely on data-driven algorithms. Yet the long-term impact of algorithmic decision making is largely ill-understood, and there exist serious challenges to ensuring equitable benefits, in theory and practice. In this thesis, I examine the social dynamics of machine learning algorithms from two angles: (i) long-term fairness of algorithmic decisions, and long-term stability in matching markets./nIn computer science, the subject of algorithmic fairness has received much attention, yet the understanding that algorithms can have disparate impact on populations through various dynamic mechanisms is recent. We contribute to this evolving understanding by presenting two different models of the dynamic interactions of machine learning algorithms and populations of interest. First, we introduce the notion of delayed impact---the welfare impact of decision-making algorithms on populations after decision outcomes are observed, motivated, for example, by the change in average credit scores after a new loan approval algorithm is applied. We demonstrate that several statistical criteria for fair machine learning proposed by the research community, if applied as a constraint to decision-making, can result in harm to the welfare of a disadvantaged population. Next, we consider a dynamic setting where individuals invest in a positive outcome based on their expected reward from an algorithmic decision rule. We show that undesirable long-term outcomes arise due to heterogeneity across groups and the lack of realizability, and study the effectiveness of interventions such as 'decoupling' the decision rule by group and providing subsidies./nAdjacent to the question of long-term fairness, another challenge faced in the utilization of machine learning for societal benefit is that of social choice. In markets, individual learning objectives---typically conceived---may conflict with the long-term social objective of reaching an efficient market outcome. Motivated by repeated matching problems in online marketplaces and platforms, we study two-sided matching markets where participants match repeatedly and gain imperfect information about their preferences through matching. Due to competition, one participant's attempt to learn their preferences may affect the utility of other participants. We design a machine learning algorithm for a market platform that enables the market as a whole to learn their preferences efficiently enough to quickly attain a notion of market fairness known as stability. Further, we study a decentralized version of the aforementioned problem, and design learning algorithms for participants to strategically avoid competition given past data, thus removing the need for a central platform. We also investigate whether strategic participants with the temptation to act independently should still follow the algorithm's recommendations, showing several positive results on the algorithms' incentive compatibility.

Advisor: Hardt, Moritz
Jordan, Michael I
Harnessing rare actinomycete interactions and intrinsic antimicrobial resistance enables discovery of a metabolic inhibitor with an unusual mode of action

Bacterial natural products have historically been a deep source of new medicines, but their slowed discovery in recent decades has put a premium on developing strategies that enhance the likelihood of capturing novel compounds. Here, we used a straightforward approach that capitalizes on the interactive ecology of ‘rare’ actinomycetes. Specifically, we screened for interactions that triggered the production of antimicrobials that inhibited the growth of a bacterial strain with exceptionally diverse natural antimicrobial resistance. This strategy led to the discovery of a family of antimicrobials we term the dynaplanins. Heterologous expression enabled identification of the dynaplanin biosynthetic gene cluster, which was missed by typical algorithms for natural product gene cluster detection. Genome sequencing of partially resistant mutants revealed a 2-oxo acid dehydrogenase E2 subunit as the likely molecular target of the dynaplanins, and this finding was supported by computational modeling of the dynaplanin scaffold within the active site of this enzyme. Thus, this simple strategy, which leverages microbial interactions and natural antibiotic resistance, can enable discovery of molecules with unique antimicrobial activity. We aim to adapt this strategy into a microfluidics platform to improve its throughput. In this regard, we have established a prototype of this device in its final stages, though some minor adjustments need to be made before undertaking a full screen. Overall, these results highlight the strength of this screening strategy and indicate that primary metabolism may be a direct target for inhibition via chemical interference in competitive microbial interactions.

Advisor: Traxler, Matthew F
Mechanical Properties and Failure Mechanisms of Advanced Structural Materials

The advancement of structural materials with different engineering applications is dependent onunderstanding their mechanics and mechanisms of deformation and fracture. The performance of materials under extreme environmental conditions is the limiting factor in many engineering systems: from the jet engine, to nuclear power plants, to something as mundane as a bridge or building. In this study, three structural materials are examined from the perspective of understanding how their unique microstructures lend them their ability to mechanically withstand the extreme environments they are designed for. First, Deformed and Partitioned (D&P) steel is discussed. D&P steel possesses good mechanical properties: a yield strength around 2GPa and a fracture toughness as high as 100 MPa√m. The way D&P steel is processed produces a highly tailored martensite/austenite duplex microstructure. This microstructure allows for both transformation induced plasticity (TRIP) and delamination toughening to forego the strength-toughness trade-off, without the addition of expensive alloying elements. Next, Tristructural isotropic (TRISO) nuclear fuel particles tested with the ALS tomography beamline are described. Due to the complex layered microstructures, in situ tomography is required to examine the internal features and failure mechanisms of the particles while being deformed at room temperature and 1000 ℃. This technique is used to examine the change in failure loads and fracture mechanisms due to the presence or absence of a SiC layer within the particles. For the TRISO particles, the results show that the SiC layer is responsible for a decrease in strength at higher temperatures due to the relaxation and redistribution of residual stresses. Finally, the mechanical properties, microstructural characterization, and failure mechanisms of body-centered cubic refractory high entropy superalloys (Ti20Zr20Nb25Ta25Al10) are provided for two differing heat treatments. The first heat treatment has a microstructure with a brittle matrix and a ductile precipitate, whereas the second is inverted, having a ductile matrix and brittle precipitate. These two heat treatments were then examined in compression, tension, and fracture toughness at room and elevated temperatures. These materials have high yield strengths and ductility in compression, yet they are brittle in tension and have low fracture toughness values at all temperatures. Both heat treatments were brittle in tension and failed intergranularly because a ductile phase with a smaller fraction of the secondary strengthening precipitate phase formed at the grain boundaries which weakened the material.

Advisor: Ritchie, Robert O
Optimization: Stochastic thermodynamics, machine learning, and numerical algorithms

Optimization is a natural language in which to express a multitude of problems from all reaches of the mathematical sciences. This is a dissertation in three parts, detailing work on three different problems in optimization./nWe begin in stochastic thermodynamics, with the problem of computing optimal operating protocols for Brownian machines by minimizing their dissipation on average. We develop a perturbative solution to the Fokker-Planck equation for one-dimensional driven Brownian motion in the limit of slow driving. Calculating the average dissipation in this formalism, we demonstrate the existence of an emergent Riemannian geometric structure in the space of control parameters, whereby the $n^{th}$ order term in the average dissipation contains a tensor of order $n$. We rigorously derive an exact formula for the order-$2$ tensor (the metric) and show that up to second-order terms in the perturbation theory, optimal dissipation-minimizing driving protocols minimize the length defined by this metric./nNext, we move to the world of machine learning, and study the generalization properties of models trained with first-order optimizers on whitened data, and with exact second-order optimizers. For a general class of models trained with first-order optimizers, we find that the mutual information between the trained model and the dataset is strictly smaller when the data is whitened than when it isn't. This reduction of access to information relevant for test performance negatively impacts generalization in a dimension-dependent manner, worsening for high dimensional problems. Exploiting analogies between training linear models and wide neural networks with a first-order optimizer on whitened data and with an exact second-order optimizer, we also establish the existence of a generalization gap between first- and exact second-order optimizers in these model classes. Experiments demonstrate that our conclusions hold in a larger class of models than is supported by the theoretical results, but that regularizing a second-order optimizer appropriately can compensate for the reduction in generalization performance observed in the exact setting./nFinally, we study the problem of setting step sizes for discrete-time gradient-based optimization. Taking the view that a discrete-time optimizer is a simulation of an underlying ordinary differential equation (ODE), we set step sizes for the former by controlling the error with which it tracks the latter. The resulting adaptive step size mechanism is highly efficient, requiring just one extra gradient call over all iterations, and applicable to any optimizer with a well-defined continuous-time representation as an ODE. We find empirically that this step size routine can achieve oracle lower bounds on strongly convex functions for first- and second-order optimizers, and that it can also perform competitively with the state of the art on a nonconvex problem, principal components analysis.

Advisor: Jordan, Michael I
DeWeese, Michael R
Discovery of Nanoscale Biophysics through Super Resolution Microscopy

Unveiling the nanoscale structural mechanism is believed to be key to understanding the nature of biology. In the last 15 years, the advancement of super-resolution microscopy, especially STochastic Optical Remonstration Microscopy (STORM) or Single Molecule Localization Microscopy (SMLM), equivalently brought the resolution of optical microscopy down to ~10nm and thus enabled many disrupting biological discoveries. In this dissertation, I initially show how the spatial resolution of optical microscopy could be largely increased through the development of photo-switchable fluorophores and a single molecule localization algorithm. Then, I use two interesting examples to show how STORM could provide a new angle in fundamental cell biology research. First, I illustrate the discovery of a novel structural model of the tubular endoplasmic reticulum as well as its regulation mechanism by curvature formation protein, Rtn4, and luminal bridge, Climp63. Second, I apply STORM to demonstrate the actin-associated vesicle scission mechanism of clathrin-coated pits during eukaryote endocytosis. In the last Chapter, I make some future outlook the in the recent development of functional super-resolution microscopy, which not only achieves higher spatial resolution but also encode useful physicochemical insight in the image.

Advisor: Xu, Ke KX
NumS: Scalable Array Programming for the Cloud

Scientists increasingly rely on Python tools to perform scalable distributed memory arrayoperations using rich, NumPy-like expressions. Existing solutions achieve sub-optimal performance on numerical operations and training of machine learning models by relying on dynamic scheduling provided by task-based distributed systems. This can lead to performance problems which are difficult to address without in-depth knowledge of the underlying distributed system. In particular, generalized linear models are difficult to scale given their reliance on element-wise array and basic linear algebra operations./nIn this thesis, I present these problems in terms of scalable linear algebra and automatic parallelization of Python. The solutions presented seamlessly scale the NumPy API and generalized linear models on task-based distributed systems. Our overall solution is presented in three primary parts: (1) An approach to parallelizing generalized linear models (GLMs)using blocked matrix operations. (2) The open source library NumS, an implementation of these ideas for the NumPy API optimized for the distributed system Ray. (3) Formal syntax and semantics for automatic parallelization of basic Python and linear algebra operations./nOur primary contribution is NumS, a modular Python-based distributed numerical array library optimized for Ray. Load Simulated Hierarchical Scheduling (LSHS), the scheduler developed for NumS, is capable of attaining communication lower bounds on some common numerical operations. Our empirical study shows that LSHS enhances performance on Ray by decreasing network load by a factor of 2×, requiring 4× less memory, and reducing execution time by 10× on the logistic regression problem. In a comparison to related solutions, LSHS achieves up to 2× speedup on logistic regression compared to Dask ML and Spark’s MLlib on a terabyte of data.

Advisor: Jordan, Michael I
Stoica, Ion
Charge Carrier Dynamics in Metals and Metal-Semiconductor Junctions Studied by Transient Absorption and Photoemission Spectroscopy

Transient spectroscopy enables the observation of fundamental processes that bring an out of equilibrium material back to its original, equilibrium state. In the most general terms, transient spectroscopy requires varying the time delay between two pulsed beams of light, where one beam perturbs the sample out of equilibrium and the other beam observes the material response. Through the discovery of high harmonic generation of extreme ultraviolet (XUV) light, transient spectroscopy gained the element specificity provided in the x-ray regime, while retaining the excellent time resolution afforded by fiber broadened, compressed laser light. Throughout the course of this thesis, the capabilities of transient XUV absorption and photoemission spectroscopy will be discussed. New insights into the charge carrier dynamics of metals and metal-semiconductor junctions will be gained through the employment of these techniques./nIn chapter 1 the fundamental principles of absorption, photoemission, and high harmonic generation will be laid out. The transient XUV absorption and photoemission spectroscopy apparatuses will be shown in chapter 2 and 3, respectively. Thin film growth of metals and metal-oxide is discussed in chapter 4. Attosecond XUV transient absorption is then used to provide insight into the dependence electron thermalization rates on the band structure of metals in chapter 5. In chapter 6, transient XUV absorption spectroscopy is used to study electron transfer in a Pt/TiO2 heterostructure. Finally, transient photoemission spectroscopy is used to study hole transport in Zn/GaP junction.

Advisor: Leone, Stephen R
IKUNA: Articulating Stories and Knowledge of Indigenous Oceanic Roots and Routes

This dissertation examines the work of diasporic Pacific Islanders to reconnect with lands/seas of Oceania and suggests storytelling, place-making and multimodal creativity as intrinsic to cultural reclamation education. The research is derived from a community engaged project of a Pacific Islander educational non-profit in developing a travel program for cultural exchanges between Northern California and Hawai’i. Research was conducted using ethnographic observations, interviews and participant generated art projects to generate a diasporic pedagogy and explore concepts of cultural identity, belonging and reclamation. This work enters at the nexus of “rooted” place-based educational practices and adaptivity of “routed” migrancy pedagogies in service of decolonial potentiality. It argues that cultural reclamation pedagogy in diaspora can be guided by both pedagogies in order to refashion connections across time and geographies to exceed colonial containment logics of modernity and Indigeneity. It also contends that a practice of ARTiculation guides these efforts through multisensory meaning-making and multi-modal generation.

Advisor: Baquedano-López, Patricia
Stochastic Models of Geodynamo Simulations

The Earth's magnetic field originates primarily in the interior of the planet, and represents one of the few signals through which processes of the deep Earth are expressed at the surface. Observations and paleomagnetic measurements of the geomagnetic field therefore provide a valuable means of investigating the state and dynamics of the Earth's core. Fluctuations in the geomagnetic field occur on a wide range of timescales; however, the inaccessibility of the deep Earth means that these fluctuations are poorly understood. To investigate how external magnetic field variability relates to processes in the core, recent studies have constructed stochastic models from direct numerical simulations of the geodynamo. If these stochastic models reflect the underlying dynamics, then they may be used to characterize the paleomagnetic record and investigate the state of the Earth's core. In this thesis, I use geodynamo simulations in conjunction with stochastic models to investigate the expression of internal core processes on external field fluctuations. I examine how changes in the style and vigor of convection are reflected in stochastic models of axial dipole field variability. I find that the magnitude of variability is linked to dipole field generation, whereas the average regression to the mean is associated with turbulently enhanced magnetic diffusion. I also construct a stochastic model from a reversing geodynamo simulation, and find that stochastic models can approximately reproduce the statistics of polarity reversals. Finally, I develop a set of statistical techniques to fit stochastic processes to irregularly sampled time-series data, which are common in paleomagnetic measurements. The overall goal is to investigate the use of stochastic models as a means to relate external magnetic field variability to internal mechanisms in planetary dynamos.

Advisor: Buffett, Bruce
Using integrable colored vertex models to study LLT polynomials, super ribbon functions, and domino tilings of the Aztec diamond

The study of integrable systems - also known as vertex models, ice models, or multiline queues - is a classical subject. Recently they have enjoyed an advent into the world of (non)symmetric polynomials, and have been generalized to colored vertex models and polyqueue tableaux. In this thesis, we study three topics in algebraic combinatorics - LLT polynomials, super ribbon functions, and domino tilings of the Aztec diamond - using several Yang-Baxter integrable colored vertex models./nEmploying the ``white" vertices, we construct a certain class of partition functions that we show are equal to the LLT polynomials of Lascoux, Leclerc, and Thibon. We then give alternate proofs of many properties of these polynomials, including symmetry and a Cauchy identity, using the vertex model formalism./nEmploying the ``white" and ``purple" vertices, we construct a certain class of partition functions that we show are essentially equal to the super ribbon functions of Lam. This construction generalizes our construction of partition functions equal to the LLT polynomials which employ just the ``white" vertices. We then give proofs of many properties of these polynomials, namely a Cauchy identity and generalizations of known identities for supersymmetric Schur polynomials, using the vertex model formalism./nFinally, we study k-tilings (k-tuples of domino tilings) of the Aztec diamond. We describe two models - one based on the ``purple" and ``gray" vertices and one based on the ``white" and ``pink" vertices - for assigning a weight to each k-tiling, depending on the number of dominos of certain types and the number of ``interactions" between the tilings. We compute the generating polynomials of the k-tilings in both models, as well as the arctic curves of the k-tilings in certain limits of the interaction strength.

Advisor: Corteel, Sylvie