VictorJuiz/thesis huggingface dataset:
In silico chemistry : multiscale modeling from quantum mechanics to kinetics

Chemical phenomena occur across a wide range of length and time scales. Atoms vibrate on the order of 10-15 seconds, while chemical reactions may take place anywhere between 10-12 seconds and hours (or as long as a chemist is willing to wait). Quantum mechanics governs the smallest of these scales, where the Schrödinger equation tells us how to compute observable properties of quantum systems. For such small systems, highly accurate calculations can be performed, though often with formidable computational cost. At intermediate scales with hundreds of atoms, approximate quantum mechanical methods can be used to define potential energy surfaces, which can be used in conjunction with Newtonian mechanics to simulate the trajectories that molecules move in. Kinetic models bridge to even larger, continuum scales, by utilizing a set of differential equations that relate macroscopic thermodynamic variables, such as temperature and pressure, to the concentrations of chemical species. The differential equations can be integrated to arbitrary points forward in time, providing predictions of how thermodynamic variables and concentrations change. Each of the aforementioned models yields a trade-off: high accuracy can be attained in the limit of the very small, while in the limit of the large, we are restricted to using coarser models. Connecting microscopic descriptions of molecules with models of macroscopic phenomena, as well as the reverse, can be informative for models at both scales. This dissertation aims to do precisely this, progressively working our way up from small length and time scales to macroscopic scales. First, we discuss research on improving the performance of electronic structure calculations such as Hartree-Fock by constructing approximations to the wavefunction through partitions of atoms into fragments. Then, we show how machine learning can be utilized to approximate effective Hamiltonian matrix elements. After, we discuss molecular dynamics integration, deriving multiple time step integration methods, showing how explicit integrators can be easily implemented, and how the accuracy of symplectic integrators can be analyzed in terms of the shadow Hamiltonian. Last, we make the connection of accelerated molecular dynamics simulations of nitromethane to kinetic models, which can be used to predict reaction pathways with varying thermodynamic conditions and time scales, allowing for comparison of predictions to experiments on computationally challenging time scales

Keywords: Chemistry.
Advisor: Martinez Todd J. (Todd Joseph)
Dai Hongjie
Markland Thomas E.
In situ visualization and nanophotonic control of hydrogen-driven nanoscale phase transformation dynamics

Understanding and manipulating chemically-driven nanoparticle transformations is crucial for the efficacy of next-generation energy storage and catalytic materials. However, obtaining such control is an outstanding challenge. Notably, there is a mismatch in length-scale between the atomic-scale structural features (such as atomic coordination number and surface strain) which influence transformation dynamics and the extrinsic parameters (such as temperature, chemical composition, and chemical environment) that can be controlled. In this thesis, I explore how optical excitation can be used to enable new catalytic mechanisms. In particular, we utilize local surface plasmon resonances (LSPRs), which result in strong optical enhancement, local temperature gradients, and hot carrier populations, to control chemical transformations. These LSPRs enable chemical activity that is no longer solely defined by inherent nanoparticle structure but rather by the local electromagnetic field. We use a suite of in situ environmental transmission electron microscopy techniques to visualize a model reaction: the hydrogenation and dehydrogenation phase transformation of individual palladium hydride (PdHx) nanoparticles. First, I describe the mechanism of this phase transition without plasmonic excitation. By visualizing the phase transformation dynamics in distinctly-faceted nanoparticles, including single-crystalline cubes and octahedra, we find that the new phase always nucleates at nanoparticle tips and corners. Then, I demonstrate how LSPRs can modify the preferred active site using a proof-of-concept crossed-bar Au-PdHx system. Under resonant illumination, we observe new nucleation sites and dynamics at the nanoscale, which we experimentally and computationally verify to be energetically unfavorable. Our work shows how plasmons enable catalytic site-selectivity and new reaction mechanisms, with important implications spanning catalysis, energy, and information

Keywords: Applied Physics.
Advisor: Byer R. L. (Robert L.)
Dionne Jennifer Anne
Brongersma Mark L.
In vivo and in vitro protein folding : novel insights in co-translational protein folding and computational protein design

This thesis focuses on characterizing the determinants of ribosome pausing and its correlation with the mechanisms of protein folding by combining machine learning methods with ribosome profiling. Furthermore, for the first time, a detailed explanation of the intricate manner on which the cotranslational machinery assists the folding of newly synthesized proteins is offered. Finally, a demonstration of how information provided by naturally occurring proteins can be leveraged in order to design new protein-protein interactions will be provided

Keywords: Biology.
Advisor: Frydman Judith
Feldman Marcus W.
Gozani Or Pinchas
Influence of reinforcing steel fracture on seismic performance of concrete structures : fracture simulation, earthquake duration effects, and design strategies

Low-cycle fatigue and fracture of longitudinal reinforcing steel is a critical potential failure mode in concrete structures subjected to earthquake ground motions. Design to resist fracture depends on the reinforcing steel materials, the design details of the reinforced concrete members, and the cyclic deformation and strain demands imposed by the earthquake. Recently, concerns related to reinforcing bar fatigue and fracture have arisen due to two developments. One is the interest by the engineering and construction industry to utilize high-strength reinforcing steel materials in structures designed for regions with high seismic hazard. Since high-strength reinforcing bars tend to be less ductile and more prone to low-cycle fatigue, as compared to conventional Grade 60 bars, their adequacy for use in structures in high seismic regions needs to be confirmed. The second is related to concerns about long duration ground motions, which can increase the cyclic loading demands on steel reinforcement. Since current design methods do not explicitly consider the influences of ground motion duration, questions have been raised as to whether current seismic design requirements are adequate for structure in regions that may be affected by large magnitude earthquakes or geologic basin effects, which can lead to ground motion duration that are longer than considered in conventional designs. In the prevailing structural design and assessment methods, fatigue and fracture are only implicitly considered by checks of peak deformation measures (e.g., peak strain demand) or other proxy measures (e.g., cumulative plastic demand), which are not sufficient to resolve behavioral effects associated with material cyclic toughness and duration effects under random cyclic loading. This study (1) develops a reliable analytical framework and supporting computational tools for simulating fatigue and fracture in steel reinforcement, considering the steel material properties, reinforcing bar details in concrete structures, and random ground motion loading; (2) applies these methods to evaluate seismic design requirements for high-strength reinforcing bars, specifically Grade 80 and Grade 100 bars; (3) develops an analytical framework and algorithms for using nonlinear dynamic analysis results to systematically evaluate earthquake duration and ground motion spectral shape effects on structures; and (4) applies these methods to incorporate earthquake duration effects in the design of reinforced concrete bridge piers

Advisor: Deierlein Gregory
Baker Jack
Billington Sarah
Information-directed sampling for reinforcement learning

Reinforcement learning has enjoyed a resurgence in popularity over the past decade thanks to the ever-increasing availability of computing power. Many success stories of reinforcement learning seem to suggest a potential gateway to creating intelligent agents that are capable of performing tasks with human-level proficiency. However, many state-of-the-art reinforcement learning algorithms require a tremendous amount of simulated data, which is not practical when data is generated from actual interactions in the real world. Addressing data efficiency will be crucial for making reinforcement learning practical for real-world applications. In this dissertation, we take an information-theoretic approach to reason about how an agent should acquire information in an environment to improve decision-making. We generalize the information-directed sampling (IDS) decision rule from online decision-making literature to reinforcement learning. This decision rule aims to acquire useful information about the environment while also taking into consideration the costs of information acquisition. We argue that IDS can demonstrate desirable information-seeking behaviors in a reinforcement learning problem where existing methods fail. We hypothesize that in practical environments that are typically rich in observations, IDS has the potential to significantly improve data efficiency relative to existing exploration schemes. Furthermore, we analyze the expected regret of IDS for three stylized classes of environments, linear bandits, tabular Markov decision processes (MDPs), and factored MDPs. We derive regret bounds that are nearly competitive with state-of-the-art regret bounds, which demonstrate promise of our information-theoretic design concept. Lastly, the form of IDS studied in this dissertation should be viewed as an agent design concept rather than a concrete algorithm. Major work needs to be done to design practical algorithms that preserve the benefits of this conceptual decision rule while being computationally tractable. We highlight some key aspects for designing a practical IDS agent and propose several research directions for addressing each aspect

Keywords: Management Science and Engineering.
Advisor: Van Roy Benjamin
Brunskill Emma
Johari Ramesh
Inroads into the Global Cyberspace: The Visions and Prospects of China's Digital Silk Road

The thesis aims to understand the visions for China’s Digital Silk Road and its prospects for success in engaging other countries in this initiative. The Digital Silk Road (DSR) is a recent extension of the Belt and Road Initiative (BRI), aimed at the non-physical aspects of China’s “Going Out” and the establishment of trade networks and infrastructure. The DSR is a clear departure from the BRI and physical infrastructure, with its emphasis on the development and export of digital technologies. There are three broad categories of DSR projects: Digital Infrastructure; Smart City Development; E-Commerce. As Chinese technology develops, we are likely to see the scope of DSR projects increase as more disruptive technologies become products of Chinese export. It is clear that the DSR constitutes the technological thrust of the BRI and China’s “going out” policy. This global policy of exporting technology is also a rather unprecedented move by China and deserves scholarly attention. The DSR offers an insightful avenue to understand China’s international goals and aspirations with respect to the BRI and in the global cyberspace. This thesis focuses on Southeast Asia as a case study to understand the prospects of foreign engagement in such initiatives. It is concerned with these fundamental questions: What are China’s visions and goals with respect to the DSR? Why and how are these vision(s) attractive to other countries? Does China have the political, economic, and technological capacity to accomplish all this on a sustained basis?

Keywords:  Stanford Global Studies
Stanford Global Studies
East Asian Studies
Belt and Road Initiative
Digital Silk Road
China
Southeast Asia
Technology
Foreign Policy
International Relations
Trade
Security
Thesis
Advisor: Lampton David M.
In-situ adaptive reduction of nonlinear multiscale structural dynamics models

A computational framework of the in-situ, adaptive Projection-based Model Order Reduction (PMOR) is proposed for solving a general nonlinear dynamic multiscale problem. This framework focuses on the reduction at the microscale by using the method of database of local Reduced-Order Bases (ROBs), which is constructed and updated on-the-fly to avoid the weakness of extrapolation in the conventional non-adaptive training. Instead of performing a classical offline-online training approach, an online algorithm is designed for this adaptive process. The proposed framework treats the deformation gradient, defined at each macroscale material point, as a vector parameter domain. This characterization enables it to locate each discrete boundary value problem of the microscale Representative Volume Element (RVE) in the database and assign to it online the most appropriate local ROB, by computing the distance from the query point to the centroid of ROB in the parameter domain. Excessively large local ROB will be split into two new local ROBs to achieve a computational efficiency. The model accuracy is controlled by sampling new information from a High Dimensional Model (HDM) and updating the existing local ROB. Performance results obtained for two computationally intensive nonlinear, dynamic, two-level multiscale applications reveal that the proposed computational framework for the in-situ, adaptive microscale projection-based model order reduction is capable of delivering wall-clock time speedup factors while maintaining a desired level of accuracy

Keywords: Mechanical Engineering.
Advisor: Farhat Charbel
Instrumenting and supporting college course-selection decision-making processes

Many U.S. universities encourage undergraduates to consider a variety of academic options before committing to a field of study. Through course-selection decision-making processes, students gravitate toward particular ideas, skills, and careers and away from others. Yet despite the consequences of these processes, scientific studies of the processes have been particularly limited. A team of three professors, a PhD student (the dissertation author), two Master's students, and six undergraduates have developed the first version of Carta in two years to instrument course-selection decision-making processes. Launched in August 2016 at a selective private research university, Carta is a course exploration platform based on university enrollment data and end-of-course evaluation survey data. As of December 2019, 98% of the current undergraduates have used Carta. We introduced a framework for understanding the students' elective choice by modeling it as a longitudinal funnel, with course consideration as an intermediary stage between course availability and course choice. Then we leveraged Carta to observe course consideration empirically. To examine how students' exposure to systematic information about available academic options influences their course consideration, effort regulation, and performance, we conducted two large-scale field experiments. In the first experiment, all the undergraduates on campus were randomized to an encouragement to use Carta. In the second experiment, they were randomized to an exposure to grade distributions and self-reported time commitment distributions of prior students. The results demonstrated that information about prior students' grade distributions lowered current students' overall GPA by 0.16, or approximately half the distance between, for example, a B+ and a B. The elective system makes it easy for students to avoid exploring courses due to issues such as lower grades and fitting in. Aiming to alleviate these concerns, we implemented a writing exercise on Carta that asks students to reflect on their core values during course consideration. Our longitudinal field experiment shows that the activity increased exploratory enrollment by 15% for majors with requirement units below the median, the majors that coincide with humanities and sciences majors. These studies point to a future of data-driven higher education in which student data, research and experimentation, teaching and learning, and university administration are all tightly connected to continually improve the experience for all participants--students, faculty, and administrators alike. Carta is a living computational social system in which one can design, test, learn, and repeat

Advisor: Johari Ramesh
Mitchell John C. 
Bernstein Michael S.
Stevens Mitchell L.
Integrated stabilization and collision avoidance for automated vehicles in emergency scenarios

As automated vehicles become more prevalent and capable, venturing out of research labs and onto public streets, they present an opportunity to further improve the safety of their passengers and other road users. This dissertation focuses on safety through the lenses of lateral stabilization and collision avoidance. First, a steering controller that integrates both of these responsibilities is developed, enabling the controller to deviate from a reference path to satisfy stabilization criteria while ensuring such deviations are made safely with respect to obstacles and road edges. Next, this concept is expanded to develop a controller that simultaneously plans lateral and longitudinal forces directly for the purposes of collision avoidance. Finally, the sensitivity of the stability of system to disturbances is analyzed, motivating a controller which models this sensitivity by propagating uncertain disturbances along a prediction horizon in the lateral velocity states. By minimizing this open-loop velocity uncertainty, the controller can plan and execute necessarily aggressive collision avoidance trajectories while reducing its sensitivity to modeling error

Keywords: Mechanical Engineering.
Advisor: Gerdes J. Christian
Kochenderfer Mykel J.
Pavone Marco
Interfaces for efficient software composition on modern hardware

For decades, developers have been productive writing software by composing optimized libraries and functions written by other developers. Though hardware trends have evolved significantly over this time---with the ending of Moore's law, the increasing ubiquity of parallelism, and the emergence of new accelerators---many of the common interfaces for composing software have nevertheless remained unchanged since their original design. This lack of evolution is causing serious performance consequences in modern applications. For example, the growing gap between memory and processing speeds means that applications that compose even hand-tuned libraries can spend more time transferring data through main memory between individual function calls than they do performing computations. This problem is even worse for applications that interface with new hardware accelerators such as GPUs. Though application writers can circumvent these bottlenecks manually, these optimizations come at the expense of programmability. In short, the interfaces for composing even optimized software modules are no longer sufficient to best use the resources of modern hardware. This dissertation proposes designing new interfaces for efficient software composition on modern hardware by leveraging algebraic properties intrinsic to software APIs to unlock new optimizations. We demonstrate this idea with three new composition interfaces. The first interface, Weld, uses a functional intermediate representation (IR) to capture the parallel structure of data analytics workloads underneath existing APIs, and enables powerful data movement optimizations over this IR to optimize applications end-to-end. The second, called split annotations (SAs), also focuses on data movement optimization and parallelization, but uses annotations on top of existing functions to define an algebra for specifying how data passed between functions can be partitioned and recombined to enable cross-function pipelining. The third, called raw filtering, optimizes data loading in data-intensive systems by redefining the interface between data parsers and query engines to improve CPU efficiency. Our implementations of these interfaces have shown substantial performance benefits in rethinking the interface between software modules. More importantly, they have also shown the limitations of existing established interfaces. Weld and SAs show that a new interface can accelerate data science pipelines by over 100x in some cases in multicore environments, by enabling data movement optimizations such as pipelining on top of existing libraries such as NumPy and Pandas. We also show that Weld can be used to target new parallel accelerators, such as vector processors and GPUs, and that SAs can enable these speedups even on black-box libraries without any library code modification. Finally, the I/O optimizations in raw filtering show over 9x improvements in end-to-end query execution time in distributed systems such as Spark SQL when processing semi-structured data such as JSON

Advisor: Zaharia Matei 
Kozyrakis Christoforos
Winstein Keith
Interferometric imaging of electrical signaling in biological cells

Electrical signaling in biological cells is an important mediator of physiological functions, ranging from graded potentials in sensory neurons and other cells to action potentials in motor- and other neurons in the nervous system. The action potential constitutes the basic unit of neural information, and it is of particular interest to detect. Classically, this is done using invasive electrical measurements, such as a single-cell patch clamp or arrays of extracellular electrodes. More recent optical methods require exogenous markers, such as fluorescent calcium indicators, or genetically encoded transmembrane voltage sensors, and are thus still invasive. Truly non-invasive optical methods based on single-point laser interferometry have demonstrated nanometer-scale cellular deformations that accompany electrical events. This phenomenon can be exploited as an all-optical method for detection of action potentials and other electrical signaling. Here, I report full-field imaging of cellular deformations accompanying the action potential in mammalian neuron somas (-1.8 nm to 1.4 nm) and neurites (-0.7 nm to 0.9 nm), using high-speed common-path quantitative phase imaging with a temporal resolution of 0.1 ms and an optical path length sensitivity of < 4 pm per pixel. Spike-triggered averaging, synchronized to electrical recordings, demonstrates that the time course of the optical phase changes closely matches the dynamics of the electrical signal. Utilizing the spatial and temporal correlations of the phase signals across the cell, the detection and segmentation of spiking cells can be enhanced compared to the shot-noise limited performance of single pixels. These full-field observations of the spike-induced deformations shed light upon the electromechanical coupling mechanism in all electrogenic cells and open the door to non-invasive label-free imaging of neural signaling. Such electrically-induced cellular deformations can also be seen in photoreceptors during phototransduction in the human retina. Thus, wide-field label-free optical monitoring of neural activity using fast interferometry is an imminent diagnostic tool for noninvasive assessment of retinal function, and might find broader applications in neuroscience in general

Keywords: Electrical Engineering.
Advisor: Palanker Daniel
Solgaard Olav
Wetzstein Gordon