VictorJuiz/thesis huggingface dataset:
Missile Motivations: Assessing Russian Noncompliance with the Intermediate-Range Nuclear Forces Treaty

The Intermediate-Range Nuclear Forces (INF) Treaty was a landmark bilateral arms control agreement signed by the United States and the Soviet Union in 1987, lauded for its substantial contribution to disarmament efforts. The United States formally withdrew from the Treaty on August 2, 2019, citing years of Russian noncompliance via the 9M729 missile system. This thesis examines the underlying motivations behind Russia’s development of this ground-launched, intermediate-range missile. Four hypotheses are presented: perception of traditional military threats from NATO and the United States; perception of emerging military threats from China and neighboring regions; a desire to exert political and psychological power over Europe; and independent action by and/or influence of the Russian military-industrial complex. Two novel datasets were developed: (1) a robust timeline detailing missile development, mechanics of the violation, Russian public commentary, and U.S. response, and (2) a collection of interviews of high-level American officials and American and European academic subject experts.
Based on these analyses, I conclude that the perception of traditional threats, specifically NATO enlargement and preeminence and U.S. ballistic missile defense systems in Europe, was the most influential impetus for the development of the 9M729 missile system. Additionally, the 9M729 was seen by Russian military planners as an important response to the emerging threats from Russia’s neighbors, particularly China. The Russian military-industrial complex wanted the 9M729 in order to provide flexibility in response to an increasing number of legitimate military threats across Eurasia and believed it could develop the missile with plausible deniability. Ambitious missile modernization programs and changes in relevant political and security environments eventually prompted the Russian leadership to determine that the rewards of having the 9M729 outweighed the risks associated with violating the Treaty. Understanding the rationale behind the creation of a treaty-noncompliant weapons system is an opportunity to consider how existing and future arms control agreements can be better developed, implemented, and verified.

Keywords:  Center for International Cooperation and Security
Center for International Security and Cooperation
INF Treaty
Nuclear Arms Control
Missiles
Russia
USSR
United States
NATO
China
Thesis
Advisor: Ewing Rodney
Pifer Steven
A mixed-reality system for breast surgical planning in the operating room : magnetic resonance imaging development and initial clinical evaluation

One quarter of women who undergo breast lumpectomy to treat early-stage breast cancer in the United States undergo a repeat surgery due to concerns that residual tumor was left behind. This has led to a significant increase in women choosing mastectomy operations in the United States. We have developed a mixed-reality system that projects a 3D rendering of images from a supine breast MRI onto a patient using the first-generation Microsoft HoloLens. The goal of this system is to reduce the number of repeated surgeries by improving surgeons' ability to appreciate tumor extent before--and ultimately during--the surgical procedure. We have taken several steps toward preparing this system for eventual clinical use, including: (1) development and preliminary evaluation of a supine dynamic contrast-enhanced breast MRI protocol to acquire images that more closely match the surgical position, (2) initial clinical evaluation of a mixed-reality system for breast surgical planning in the operating room in seven patients with palpable tumors, (3) a preliminary investigation of user depth perception on two commercially available mixed-reality headsets, and (4) development of proof-of-concept skin markings printed with magnetic ink that are visible both on MRI and by eye, which could be used to track deformation and improve registration to the patient with mixed reality. Although early results are promising that mixed-reality guidance can indeed provide information about tumor size and extent, it is critical but not straightforward to align the virtual content to the patient so that tumor location is accurately perceived. More work is needed to improve the registration by accounting for tissue deformations, as well as to properly calibrate the system for different users and optimize the display for arm's-length interaction. Nonetheless, this exciting new use for mixed reality has the potential to make tumor localization more intuitive for surgeons and to improve the lives of many patients

Keywords: Bioengineering.
Advisor: Daniel Bruce (Bruce Lewis)
Hargreaves Brian Andrew
MLASL: ML-Assisted Student Learning

With the invention of machine learning methods to learn any arbitrary function using the universal approximation theorem, there has been much curiosity around the application of such learning power to the human process of learning.
In this work, the main contributions are...
- a framework to completely define the student learning problem,
- a machine learning model that uses said framework to aid student learning,
- an in-depth analysis of the framework's challenges and potential solutions,
- discussion and development of data-efficient training methods, and
- discussion and development of practical applications of this theory.
This paper calls such a framework ML-Assisted Student Learning (MLASL). Future work entails further research into the translation of information between vector spaces with large dimensionality differences, particularly in the large-to-small use case.

Keywords:  Department of Computer Science
machine learning
theory
student learning
human learning
department of computer science
stanford university
artificial intelligence
practical application
honors thesis
Thesis
Advisor: Piech Chris
Kochenderfer Mykel
Mobile genetic elements : mechanisms of microbial evolution and reservoirs of genome engineering tools

Mobile genetic elements (MGEs) are genetic material that can mobilize and move either within a single organism's genome, or between the genomes of different organisms. An integrative mobile genetic element is one that can merge its genetic material with other genetic molecules to form a single genetic molecule, either DNA or RNA. Thanks to the work of pioneering scientists such as Nobel Laureate Barbara McClintock, our knowledge of how MGEs work and how they influence evolution has exploded and continues to grow to this day. MGEs and the enzymes that enable their mobilization (transposases, recombinases, integrases, resolvases, invertases, etc.) have been adapted as useful experimental and therapeutic tools. But there is still much to be learned about MGEs, and in the age of next-generation sequencing we have more tools than ever before to interrogate their function. In chapter 2, this dissertation introduces software that analyzes next-generation sequencing data to identify integrative mobile genetic elements and to genotype the precise genomic location of their insertion with respect to a reference genome. This bioinformatics tool, called MGEfinder, is applied in an analysis of thousands of publicly-available bacterial genomes, and this analysis sheds light on how MGEs allow bacteria to adapt, develop antibiotic resistance, and become more virulent and dangerous to their human hosts. In chapter 3, this tool and others are then adapted and used as a part of a genomic data mining pipeline to identify novel genetic elements and enzymes that have promise as genome engineering tools. Thousands of these site-specific integrases (also described as recombinases) were identified and stored in a database, and they were curated and prioritized to identify those that hold promise as human genome editing tools, specifically. Early validation experiments are promising. In addition to studies that focus on this main theme of mobile genetic elements, this dissertation introduces additional works related to the identification of small open reading frames (smORFs) in microbial genomes (chapter 4), and the transcriptomic and epigenomic response of a colorectal cancer cell line to the microbial metabolite butyrate (chapter 5). Together, these chapters demonstrate the importance of developing and applying tools to the study of mobile genetic elements more broadly, indicating the great scientific and therapeutic potential of these ubiquitous genetic elements

Keywords: Genetics.
Advisor: Bhatt Ami (Ami Siddharth)
Montgomery Stephen
Bassik Michael
Sherlock Gavin
Model fidelity and trajectory planning for autonomous vehicles at the limit

Autonomous vehicles have the potential to greatly improve transportation safety by eliminating many automobile accidents, the vast majority of which are caused by human error. However, for cars to be able to avoid an accident whenever physically possible, they will have to drive at least as well as the best human drivers. Racing drivers can claim to be the best drivers in the world since, by the nature of their sport, they are forced to consistently and safely operate the vehicle at its physical limits. Autonomous racing provides an avenue to rapidly develop insights and control strategies for autonomous vehicles that are applicable to emergencies on public roads. This thesis expands the understanding of what effects must be captured for a vehicle to drive at the limits of friction. First, the impact of road topography on the vehicle's limits is discussed and modeled. Experiments with an automated vehicle show that accounting for topography-driven variation in normal load is critical for ensuring that the vehicle stays within its limits. The same simple model used to generate those insights is also useful for rapid trajectory replanning, illustrated here through examples covering obstacle avoidance and racing line optimization. This ap- proach to trajectory modification constitutes the second contribution of this thesis. While the simple model upon which the method is based captures the most funda- mental limitations of the vehicle, it is worth examining the extent to which more complex models of the vehicle's dynamics lead to better performance. An evaluation of the utility of several possible models for generating trajectories at the limit on various surfaces, including ice, wet asphalt, and dry asphalt, shows that the models' prescriptions for the optimal trajectory vary little and that all can be used success- fully. However, a significant advantage of the more complex models is that the many actuators available on modern vehicles may be used in a coordinated fashion to better accomplish the desired control objective. To this end, a novel model of the effects of a limited slip differential is incorporated into the double-track model of the vehi- cle. The insights from this work can be used to design algorithms that operate over the full range of vehicle performance, maximizing an autonomous vehicle's ability to operate skillfully when racing or safely when confronted with an emergency

Keywords: Mechanical Engineering.
Advisor: Gerdes J. Christian
Cutkosky Mark R. 
Pavone Marco
Model-based design and control of deformable robots and haptic devices

Haptic interfaces are used in training, guidance, and teleoperation to provide force and tactile feedback to a user from a virtual or remote environment. In contrast to the rigid components typically comprising haptic devices, compliant materials could enable new haptic devices that move via deformation rather than via joints. However, the design and control of devices consisting of soft or deformable components is challenging, due to the complex coupling between actuation and end-effector motion and due to a lack of sensing. This thesis considers the potential for computational model-based methods to address challenges in the design and control of deformable haptic devices. In the first half of this thesis, we introduce a new haptic shape display made of soft materials for organ simulation for medical training. The device is a continuous, fully 3D shape-changing surface that a user can touch and hold. We develop a mass-spring model of the device that allows us to understand how different pneumatic actuators affect the shape. Further, we develop an automated design algorithm based on a heuristic controller and a simulation of the device to determine how to arrange a limited number of actuators to reproduce target shapes. This device and its associated algorithms show how a model can be used to understand complex deformations due to pneumatic actuation of a compliant system. The second half of this thesis focuses on the precise control of deformable haptic devices. Two deformable kinesthetic haptic devices are introduced: a 2-DOF planar device and a 5-DOF device. In these devices, forces are transmitted from the actuators to the user via deformable transmissions which allow for easy fabrication and reduced mass and friction in the device. Using these devices, we demonstrate methods for design, sensing, and control. We derive a general formula for the mapping of actuator stiffness to end-effector stiffness and verify it using the planar device. For sensing, we present results in model-based sensing using only actuator information or using model-calibrated embedded sensors. With these techniques, we can use the deformation of the device to measure the force applied to the device without an external force sensor. We extend techniques from rigid robot workspace analysis to deformable robots. Finally, we develop control techniques for haptic rendering which compensate for device mechanics and characterize the resulting forces. Together, these results in design and control show that model-based methods can overcome some of the challenges of deformable devices to enable new haptic interfaces

Keywords: Mechanical Engineering.
Advisor: Okamura Allison 
Duriez Christian
Khatib Oussama
Modular and compositional learning for natural language understanding : implications of the transfer-interference trade-off

Core to human cognition are the lifelong abilities, developed continuously and effortlessly, to learn new expressions from a small number of examples, adapt to new types of input, and generalize creatively. However, current machine learning algorithms struggle to adapt to novel inputs while retaining existing memories, often resulting in a lack of generalization. In this thesis, this challenge is addressed by introducing a novel characterization of the problem as a trade-off between two effects: on one hand, the ability to transfer knowledge from previously learned skills to the skill in the process of being learned; on the other, the effect of interference where previously learned skills are forgotten when acquiring new ones. Using the trade-off between transfer and interference as a guide, we introduce novel deep reinforcement learning architectures involving memory and routing networks that model cognitive modularity by specializing parts of their memory to different inputs. These new architectures can learn in more realistic scenarios when combined with novel optimization techniques, and can learn end-to-end with a modular and compositional inductive bias well suited to Natural Language Processing applications. These models can be flexibly incorporated in a variety of existing architectures, and can effectively leverage external information about the examples to be successful at fine-grained semantic tasks

Keywords: Linguistics.
Advisor: Jurafsky Dan
Potts Christopher
Greene Joshua David
Lassiter Daniel
Modularity and coordination for planning and reinforcement learning

The foundational objective of the field of artificial intelligence is to build autonomous systems that can perceive their environment and take actions that maximize their ability to achieve their goals. Decision making under uncertainty is a fundamental requirement for such intelligent behavior. Various real world problems of interest like autonomous driving, virtual assistants, and disaster response are sequential decision making problems. Planning and reinforcement learning are abstractions for studying optimal sequential decision making in natural and artificial systems. Combining these ideas with deep neural network function approximation (*"deep reinforcement learning"*) has allowed scaling these abstractions to a variety of complex problems and has led to super-human performance, especially in game playing. These successes are still limited to virtual worlds with fast simulators where massive amounts of training data can be generated given enough computational resources. However, decision making in the real world requires solutions that are data efficient, capable of utilizing domain knowledge when available, and generalize to related problems. Moreover, often decision making requires decentralized execution for scalability. The concept of modularity has proven effective in a large number of fields to deal with complex systems. The key ideas driving a modular system are 1) information encapsulation and 2) coordination for integrated function. Modularity allows breaking down a complex problem into manageable units. This dissertation explores how, as designers of complex decision making systems, the principles of modular design can allow us to provide structural inductive biases and define appropriate coordination mechanisms. In the first part, we explore the concept of functional modularity in the form of agents, and how they can inform the design of large multi-agent decision making systems. In the second part, we explore the concept of temporal modularity in the form of subtasks in complicated tasks and how we can learn decomposed solutions that show improved transfer performance to related tasks. Finally, in the last part, we explore the concept of architectural modularity; how known physics can inform our neural network models of mechanical systems allowing reliable planning and efficient reinforcement learning. We find that these design principles lead to enormous data efficiency improvements and lower costs for learning and inference. Moreover, we find solutions that generalize better to related problems

Advisor: Ermon Stefano
Kochenderfer Mykel J.
Brunskill Emma
Molecular effects of the 15q13.3 microdeletion in an induced pluripotent stem cell model

The 15q13.3 microdeletion is associated with several neuropsychiatric disorders, including intellectual disability, epilepsy, autism, and schizophrenia. Previous association and functional studies have investigated the potential role of several genes within the deletion in neuronal dysfunction, but the molecular effects of the deletion as a whole remain largely unknown. In this dissertation, I explore the effects of the 15q13.3 microdeletion on genome-wide gene expression, DNA methylation, and chromatin accessibility. Chapter 1 provides an introduction to the genetics of neuropsychiatric disorders and the 15q13.3 microdeletion syndrome. Chapter 2 describes the generation and immunohistochemical characterization of induced pluripotent stem cells and induced neurons from patients with the 15q13.3 microdeletion. Chapter 2 also discusses the impact of the 15q13.3 microdeletion on gene expression and sensitivity to cisplatin-induced DNA damage. Chapter 3 addresses the effects of the 15q13.3 microdeletion on the epigenome. Chapter 4 focuses on the transcriptome-wide effects of CRISPR-Cas9 knockouts of single genes in the 15q13.3 microdeletion. Chapter 5 describes the conclusions of this thesis and future directions

Keywords: Genetics.
Advisor: Urban Alexander E. 
Bassik Michael 
Montgomery Stephen
Pritchard Jonathan (Jonathan K.)
Molecular engineering of monomeric red fluorescent proteins for biosensing applications

Fluorescent proteins (FPs) have become indispensable tools that revolutionized the study of biology. High resolution crystal structures have allowed us to understand how structure and residue positioning within the tight FP barrel relates to a specific photo or biophysical function. These properties allow us to engineer FPs with unique spectral and photochemical properties such as for brightness, maturation, photostability, and monomericity. Further manipulation of FPs based on their structure and function can enable engineering them into novel biosensors. In my thesis work, I present two new beta-barrel FPs and demonstrate their use in biosensing applications. I will also describe engineering a new class of FPs from scratch based on an enzyme. I first show the engineering and development of a monomeric cyan excitable RFP (mCyRFP1) from its dimeric predecessor CyOFP1. mCyRFP1's unique properties, including monomericity, high brightness, large Stokes shift, and large emission separation from EGFP, enables for single excitation dual wavelength imaging with green fluorescent indicators such as GCaMP6 in brain slices and in vivo. Additionally, we show that mCyRFP1 can be used as an efficient FRET donor to a far-red FP mMaroon1 due to its high quantum yield and high maturation efficiency. We show that mCyRFP1-mMaroon1 can be used for dual-color fluorescence lifetime imaging (FLIM)-FRET to image single dendritic spines undergoing structural plasticity. Secondly, I describe engineering a second generation mCyRFP1 into mCyRFP2. mCyRFP2 displays enhanced photophysical properties, including improved brightness and photostability, making it almost 2-fold brighter than mCyRFP1. mCyRFP2's improved and more complete maturation enables it to be used as an efficient fusion to a green voltage indicator, ASAP3 as a reference protein to correct for motion artifacts. We show that ASAP3-mCyRFP2 is unique in retaining efficient membrane targeting while allowing single wavelength excitation dual emission separation of both fluorophores in neurons. We further demonstrate that ASAP3-mCyRFP2 can be better used to report voltage changes using the green/red ratio than ASAP3 alone in beating cardiomyocytes. Lastly, I describe the first attempts to engineer a new class of near-infrared (NIR) fluorescent proteins based on the enzyme phycocyanobilin-ferredoxin oxidoreductase enzyme (PcyA) that oxidizes biliverdin to phycocyanobilin. I demonstrate that we can create an originally non-fluorescent enzyme into an NIR FP abolishing the enzymatic activity of PcyA while preserving PcyA's ability to bind to BV

Keywords: Bioengineering.
Advisor: Lin Michael Z.
Bryant Zev David 
Huang Possu
Molecular imaging based on target-enabled in situ ligand aggregation for monitoring therapeutic responses in vivo

In the era of precision medicine, there is a growing demand for new companion diagnostics that can be used for monitoring therapeutic responses. These companion diagnostics can guide therapy selection and reduce medical cost. This work focuses on developing molecular imaging companion diagnostics based on a platform we termed "target-enabled in situ ligand aggregation (TESLA)". The imaging probes, once enters disease site and activated by the target enzyme, can undergo intramolecular cyclization and self-assembly into nanoaggregates that can generate signal retention in cells. Based on this platform, we developed a new imaging probe scaffold and applied this scaffold for imaging the activity of β-galactosidase and caspases. We also developed a new caspase-sensitive positron emission tomography (PET) imaging tracer for monitoring tumor therapeutic responses to chemo and radio therapies in vivo. This PET tracer was also used in canine clinical trials and was approved by FDA for human studies. Lastly, we introduced a new pre-targeted imaging approach based on TESLA by incorporating the trans-cyclooctene (TCO)-tetrazine click chemistry into TESLA. By decoupling the imaging tag from the scaffold, the kinetics of probe activation and retention and the kinetics of imaging tag retention can be optimized independently. This method was demonstrated in imaging caspase activity in vivo using fluorescence and PET imaging

Keywords: Chemistry.
Advisor: Rao Jianghong
Kool Eric T. 
Xia Yan