VictorJuiz/thesis huggingface dataset:
On the basis of chromatin condensation

The basis for the remarkable structural plasticity of chromosomes has been an unresolved question since Flemming's elucidation of mitosis in the late nineteenth century. As the eukaryotic cell prepares for division, its chromatin, dispersed throughout the interphase nucleus, transforms into a set of compact, discrete, cylindrical bodies (chromatids) through which the vertical transmission of genetic information is accomplished. The formation of metaphase chromatids involves three key steps: axialization, lengthwise contraction, and condensation. Axialization has been attributed to the formation of a scaffold, long suspected on the basis of observations of denatured chromosomes and demonstrated here for the first time in native specimens without assumptions as to molecular composition. Lengthwise contraction is likely mediated by the formation of chromatin loops, as recently demonstrated by several groups. Condensation, whose basis has remained obscure, is shown to involve a volume phase transition of the chromosomal gel. The gel nature of the chromosome is rigorously demonstrated through a comparison of condensation behavior with theoretical predictions for polyelectrolyte gels. Finally, the gel nature of the chromosome is exploited to allow direct observation of chromatin fibers within a native context. Analysis of isotropically expanded chromosomes by cryo-electron tomography reveals the trajectories of chromatin fibers, over distances of ten kilobases or more, at subnucleosome resolution. The trajectories are shown to be irregular, with almost no evidence of short- or long-range order

Advisor: Kornberg Roger D.
Geiduschek E. Peter (Ernest Peter)
Levitt Michael
On the evaluation of deep generative models

Evaluation drives and tracks progress in every field. Metrics of evaluation are designed to assess important criteria in an area, and aid us in understanding the quantitative differences between one breakthrough and another. In machine learning, evaluation metrics have historically acted as north stars towards which researchers have optimized and organized their methods and findings. While evaluation metrics have been straightforward to construct and implement in some subfields of machine learning, they have been notoriously difficult to design in generative models. Several reasons emerge to explain this: (1) there are no gold standard outputs to compare against, unlike held-out test sets, (2) because of their diverse training methods and formulations, inherent model properties are difficult to measure consistently, and sampled outputs are often used for evaluation instead, (3) dependence on external (pretrained) models that add another layer of bias and uncertainty, and (4) inconsistent results without a large number of samples. As a result, generative models have suffered from noisy assessments that occupy a changing evaluation landscape, in contrast to the relative stability of their discriminative counterparts. In this manuscript, we examine several important criteria for generative models and introduce evaluation metrics to address each one while discussing the aforementioned issues in generative model evaluation. In particular, we examine the challenge of measuring the perceptual realism of generated outputs and introduce a human-in-the-loop evaluation system that leverages psychophysics theory to ground the method in human perception literature and crowdsourcing techniques to construct an efficient, reliable, and consistent method for comparing different models. In addition to this, we analyze disentanglement, an increasingly important property for assessing learned representations, by measuring an intrinsic property of a generative model's data manifold using persistent homology. The final work in this manuscript takes a step towards assessing a generative model and its different modes with a key application in mind, specifically the stylistic fidelity across different generated modes in a multimodal setting

Advisor: Ermon Stefano
Ng Andrew Y.
Bernstein Michael S.
On using formal methods for safe and robust robot autonomy

Advances in the fields of artificial intelligence and machine learning have unlocked a new generation of robotic systems---"learning-enabled" robots that are designed to operate in unstructured, uncertain, and unforgiving environments, especially settings where robots are required to interact in close proximity with humans. However, as learning-enabled methods, especially "deep" learning, continue to become more pervasive throughout the autonomy stack, it also becomes increasingly difficult to ascertain the performance and safety of these robotic systems and explain their behavior, necessary prerequisites for their deployment in safety-critical settings. This dissertation develops methods drawing upon techniques from the field of formal methods, namely Hamilton-Jacobi (HJ) reachability and Signal Temporal Logic (STL), to complement a learning-enabled robot autonomy stack, thereby leading to safer and more robust robot behavior. The first part of this dissertation investigates the problem of providing safety assurance for human-robot interactions, safety-critical settings wherein robots must reason about the uncertainty in human behavior to achieve seamless interactions with humans. Specifically, we develop a two-step approach where we first develop a learning-based human behavior prediction model tailored towards proactive robot planning and decision-making, which we then couple with a reachability-based safety controller that minimally intervenes whenever the robot is near safety violation. The approach is validated through human-in-the-loop simulation as well as on an experimental vehicle platform, demonstrating clear connections between theory and practice. The second part of this dissertation examines the use of STL as a formal language to incorporate logical reasoning into robot learning. In particular, we develop a technique, named STLCG, that casts STL into the same computational language as deep neural networks. Consequently, by using STLCG to express designers' domain expertise into a form compatible with neural networks, we can embed domain knowledge into learned components within the autonomy stack to provide additional levels of robustness and interpretability

Keywords: Aeronautics and Astronautics.
Advisor: Pavone Marco
Gerdes J. Christian
Schwager Mac
Online Payment Systems and Felony Disenfranchisement in Florida

On November 6, 2018 Florida voters passed Amendment 4, re-enfranchising an estimated 1.4 million Floridians. However, when this law took effect in January 2019, Florida’s legislature passed a law stipulating that completion of “all terms of sentence” included payment of outstanding fines and fees. For many individuals, this requirement means that their ability to vote has become contingent on their being able to discern the exact amount they owe and pay often-burdensome fines, legal fees and restitution. However, county systems to inform people of how much they owe and allow them to pay their fees vary greatly in terms of ease of use and technological advancement. This paper first evaluates which factors make a formerly incarcerated individual in Florida more likely to register to vote. I find substantially higher rates of registration for Black Floridians than any other racial group. This paper also focuses on one difference between county systems for paying off one’s fees: whether online payments are possible. My regression analysis does not find a significant effect of the presence of online payment systems on registration rates.

Keywords: disenfranchisement
felony
Florida
voting
Department of Economics
Stanford University
Thesis
Advisor: Gentzkow Matthew
An open-source framework for FPGA emulation of analog/mixed-signal integrated circuit designs

FPGA emulation is widely used to speed up simulations of digital chip designs, but the technique is difficult to extend to analog/mixed-signal (AMS) designs because they contain blocks that cannot be directly mapped to an FPGA's programmable logic. This necessitates the creation of synthesizable models for AMS blocks, a process that is time-consuming, error-prone, and expensive, as it requires a rare combination of expertise: analog modeling and FPGA implementation. Furthermore, fast AMS simulation techniques do not always work well on an FPGA, and can limit the speedup that is possible through emulation. This thesis is about my efforts to unlock the potential of FPGA emulation for AMS designs by developing new high-performance AMS emulation techniques, along with an open-source framework that makes it easy to apply those techniques. The underlying premise is that AMS emulators should take large, but precise, timesteps corresponding to digital events, leveraging compile-time precomputation to make the FPGA implementation more efficient. "Analog-only" timesteps should be avoided, since their effect on emulator performance is more severe than in a CPU-based simulation. The open-source emulation framework has been applied to six commercial designs, as well as one academic design. I focus on three of those applications in this thesis: an ADC-based high-speed link design, a firmware-controlled flyback converter, and an NFC-powered chip. Across those applications, speedups as compared to existing CPU-based simulations ranged from 2-3 orders of magnitude, creating new opportunities for pre-silicon firmware development and RTL-level verification of slow feedback loops. My hope is that this freely available, high-performance framework will help to break through the verification bottleneck in modern AMS design

Keywords: Electrical Engineering.
Advisor: Horowitz Mark (Mark Alan)
Arbabian Amin
Hanrahan P. M. (Patrick Matthew)
Open-source tools for measuring, understanding, and improving walking

Walking is a complex phenomenon that requires coordination between the brain and body. Neurological disorders like Parkinson's disease affect brain regions important for motor coordination and this results in impaired walking and severe episodes of "freezing of gait" that interrupt and halt an individual's motion. Currently, therapies for people with gait impairment and freezing in Parkinson's disease are limited, due to the little understanding of the underlying neural or biomechanical mechanisms associated with impaired gait, and the lack of standardized, patient-friendly, objective tools with which to assess impaired gait in the clinic or in natural settings over long periods of time. This dissertation describes tools to measure, understand, and improve human gait. First, we developed and validated a standardized walking task and statistical model with which to assess gait, and then used this to test the efficacy of different neurostimulation therapies for Parkinson's disease. Second, we developed and tested deep learning models to detect gait impairment using wearable inertial sensors, of which the number and placement consider both patient preferences and model performance. Third, we developed and validated a workflow to reliably measure human motion over long durations with wearable inertial sensors

Keywords: Bioengineering.
Advisor: Delp Scott
Bronte-Stewart Helen
Camarillo David
Nuyujukian Paul Herag
Optical multipass microscopy

This thesis presents theory, design, and results from a novel multipass microscope. Such a microscope is characterized by the repeated interrogation of a sample with a "recycled" imaging field: a sample is imaged, then imaged with an image of itself, an image of an image of itself, and so forth. Through multiple interrogations, a linear enhancement in phase shift and absorption imparted by the sample can be achieved. This fundamentally increases the signal-to-noise of the obtainable images, or equivalently, decreases the damage imparted on the sample for a given signal-to-noise. While at first glance the technique may seem extreme in its simplicity, the approach has a number of useful properties, particularly with respect to how noise is — or is not — introduced into a measurement. This technique is entanglement-free in the conventional sense, and it is relatively robust with respect to loss in the system, making it compatible with biological samples. Additionally, there are no strong constraints placed on the probe source, making the multipass recipe in principle compatible with both bosons and fermions, either charged or neutral. I briefly discuss how these techniques could be applied to electron microscopy

Keywords: Applied Physics.
Advisor: Kasevich Mark A.
Reis David A.
Safavi-Naeini Amir H.
Optimal guidance and control of spacecraft swarms in planetary and asteroid orbits

Spacecraft swarms are the future of space missions because of their inherent redundancy, cost efficiency, and ability to achieve previously unattainable science objectives. To date the usage of swarms has been limited by a lack of available guidance and control algorithms that are compatible with these small spacecraft qualities. This dissertation introduces novel delta-v efficient guidance and control algorithms to enable swarming missions for both planetary and asteroid orbits. Specifically, this dissertation develops new relative motion models of spacecraft in asteroid orbits and presents delta-v optimal swarm guidance and control for two nominal swarm operations: 1) swarm reconfiguration, the adjustment of the swarm relative geometry, and 2) swarm keeping, the maintenance of the swarm relative motion geometry. For guidance, convex programs are formulated that minimize swarm delta-v usage during keeping and reconfiguration phases. The programs are analyzed in depth for different dynamic environments and ROE reconfigurations to produce analytical solutions or reduce computational effort for use either on ground or on orbit. For control, this dissertation presents novel swarm reconfiguration and keeping algorithms to enable swarming of asteroids and provide safe, delta-v efficient functioning. For swarm reconfiguration control, this thesis presents two novel controllers, one for near-circular and one for eccentric orbits, based on Lyapunov theory. For keeping, this thesis develops a keeping control algorithm applicable to asteroid orbits that considers the unknown asteroid dynamic field for the first time in literature. The advancements presented in this thesis are validated extensively through various example missions with a focus on the Starling-1 mission and the ANS mission concept. The algorithms and advancements developed here demonstrate significant delta-v savings and safe operation over the mission lifetime. As such, these advances enable the usage of spacecraft swarms to increase both knowledge gained from space missions and mankind's presence in space

Keywords: Aeronautics and Astronautics.
Advisor: D'Amico Simone
Rock Stephen M.
Schwager Mac
Optimal stochastic couplings

Stochastic coupling is a powerful technique in probability theory that facilitates comparison between two probability distributions. The coupling technique is useful to bound discrepancy between two distributions, design simulation algorithms, and study asymptotic behavior of stochastic processes. In this thesis, we study the applications of stochastic coupling in the context of exact simulation and distributionally robust optimization (DRO). In the first part, we provide the first generic exact simulation algorithm for multivariate diffusions. Current exact sampling algorithms for diffusions require the existence of a transformation which can be used to reduce the sampling problem to the case of a constant diffusion matrix and a drift which is the gradient of some function. Such transformation, called Lamperti transformation, can be applied in general only in one dimension. Therefore, completely different ideas are required for exact sampling of generic multivariate diffusions. Our strategy combines techniques borrowed from the theory of rough paths, on one hand, and multilevel Monte Carlo on the other, in which the random level is coupled with the diffusion process to simulate. The second part of the thesis is dedicated to optimal transport based DRO, where the optimal transport distance is defined as the expected transportation cost under the maximal coupling between two probability measures. The DRO is a systematic modelling approach to mitigate the effect of modelling error and distributional ambiguity in stochastic optimization problems. For optimal transport based DRO, we adopt a minimax framework that minimizes the expectation of random function under the worst distribution in a distributional uncertainty set characterized by optimal transport distance. We propose a methodology which learns the optimal transport cost function in a natural data-driven way. Then, under affine decision rules and conventional convexity assumptions on the underlying loss function, we obtain structural results about the objective value function, the optimal policy, and the worst-case optimal transport adversarial model. These results expose a rich structure embedded in the DRO problem (e.g. strong convexity even if the non-DRO problem was not strongly convex, a suitable scaling of the Lagrangian for the DRO constraint, etc. which are crucial for the design of efficient algorithms). As a consequence of these results, one can develop efficient optimization procedures which have the same sample and iteration complexity as a natural non-DRO benchmark algorithm such as stochastic gradient descent

Keywords: Management Science and Engineering.
Advisor: Blanchet Jose H.
Glynn Peter W.
Ye Yinyu
Optimization in the space of measures : new techniques from optimal transport

Optimal transport is a profound and elegant tool in probability theory which measures discrepancy between probability distributions. It is a legacy of more than 200 years of mathematical progress and unites disparate areas of science-- stretching from analytic geometry to physics. Because of this, optimal transport has become a primary mechanism for developing and understanding computational methods in robust optimization, machine learning, and artificial intelligence. In this work, we provide novel results which cement this link between optimal transport and data-driven, computational methods. Our first result connects the discrete optimal transport problem with recent developments in theoretical computer science. We exploit this connection to provide novel methods for computing solutions to the discrete optimal transport problem. Moreover, these procedures achieve computational complexities that are close to optimal. Our second result applies the geometry of optimal transport to develop a novel, infinite-dimensional, Frank-Wolfe method for a large class of problems in machine learning and artificial intelligence. Despite operating on an infinite-dimensional level, our procedure is concretely implementable (with finite sample guarantees) and has iteration complexities that mimic convergence rates of finite-dimensional, first-order methods. Using several canonical examples, we also demonstrate that practical implementations of our method possess attractive convergence properties. Finally, our third result develops a strong duality theory for non-parametric model uncertainty problems-- where model uncertainty is specified in terms of optimal transport discrepancy to a set of reference distributions. This duality theory extends a long line of previous results and, most importantly, establishes a computational basis for solving these (infinite-dimensional) problems. Specifically, we establish strong duality with a dual problem that can be solved computationally, using finite-dimensional optimization techniques

Keywords: Computational and Mathematical Engineering.
Advisor: Blanchet Jose H.
Glynn Peter W.
Ye Yinyu
Optimizing gene delivery for an early cancer detection strategy

Many forms of advanced cancer remain difficult to treat, with tremendous economic and social costs. Early cancer detection holds great potential in reducing cancer mortality, with the benefits of screening already well-recognized and implemented for several cancer types, including skin, colon, and breast cancers. Meanwhile, sensitive and specific screening methods for deep-tissue, aggressive cancers such as ovarian, lung, and pancreatic cancers remain elusive. Because these cancers are located far from a surface that can be easily probed, minimally invasive methods involving blood biomarkers and imaging offer great possibilities. In this thesis, I introduce a novel two-step strategy for detecting cancer early that depends on delivery of a minicircle DNA molecular probe to a tumor. In Chapter 2, I show proof-of-concept that a secretable blood biomarker and Positron Emission Tomography (PET) imaging reporter gene can be encoded on a tumor-activatable minicircle and delivered via a cationic transfection reagent. In animal studies, the secretable blood biomarker can be detected in the blood of tumor-bearing mice compared to healthy mice, and PET reporter gene expression can be quantified at significant levels in tumors. This highlights the potential of tumor-activatable minicircles to deliver reporter genes for an early cancer detection strategy. However, improved delivery vehicles for the minicircles would benefit the strategy. In Chapter 3, I investigate the ability of extracellular vesicles to encapsulate and deliver minicircles, and in Chapter 4, I expand the toolbox of delivery vehicles to include ionizable lipid nanoparticles. Novel strategies for early cancer detection combined with more targeted tumor delivery holds great potential in reducing cancer mortality

Keywords: Bioengineering.
Advisor: Ferrara Katherine W.
Lee Jin Hyung
Paulmurugan Ramasamy
Optimizing large-scale camera analytic systems with wireless insights

Cameras are key enablers for a wide range of IoT use cases including smart cities, intelligent transportation, AI-enabled farms, and more. Many of them are connected to the Internet via wireless technologies like LPWAN(LoRaWAN, SigFox) and 5G. Conventional camera analytics systems treat the underlying network as a pipe, ignoring the fact that wireless links are lossy and wireless is a broadcast medium. First, I present Starfish that is inspired by the lossy link insight. Starfish is a resilient image compression framework design for LPWAN that processes all the information loss in the application layer, thus simplifying the overall system design. To our best knowledge, it is the first DNN-based image compression framework that runs efficiently on low-cost AIoT hardware. It could be adapted to different task objectives and datasets, and exhibits graceful degradation in lossy scenarios without the need to design and tune source/channel coding. It uses neural network architecture search to generate DNN configuration thus reducing human labor and enable generalization to diverse AIoT hardware. Such end-to-end design reuses the DNN inference pipeline as in object detection/image classification tasks thus it could be easily adopted on various DNN accelerators. Next, I present YOUO (You Only Upload Once) that leverages the broadcast insight. YOUO is an object re-identification system that shifts computation from cloud to devices for better privacy and lower network/computation cost. Each target object may have been captured multiple times by multiple cameras in large-scale camera networks. With the help of multicasting service provided in 5G eMBMS, YOUO enables efficient collaboration across cameras, thus significantly reduced network traffic by more than two orders of magnitude based on experiment results on a large-scale vehicle re-identification dataset. YOUO is 3x as efficient in bandwidth when compared with optimal single-camera filtering with minimal accuracy loss while achieving 70%+ accuracy when uploading only once per object (the absolute minimum) for the entire camera network

Keywords: Electrical Engineering.
Advisor: Katti Sachin
Asgar Zain 
Prabhakar Balaji