VictorJuiz/thesis huggingface dataset:
An open-source framework for FPGA emulation of analog/mixed-signal integrated circuit designs
Keywords: Electrical Engineering.

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
Open-source tools for measuring, understanding, and improving walking
Keywords: Bioengineering.

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
Optical multipass microscopy
Keywords: Applied Physics.

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
Optimal guidance and control of spacecraft swarms in planetary and asteroid orbits
Keywords: Aeronautics and Astronautics.

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
Optimal stochastic couplings
Keywords: Management Science and Engineering.

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
Optimization in the space of measures : new techniques from optimal transport
Keywords: Computational and Mathematical Engineering.

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
Optimizing gene delivery for an early cancer detection strategy
Keywords: Bioengineering.

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
Optimizing large-scale camera analytic systems with wireless insights
Keywords: Electrical Engineering.

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
Optimizing remote procedure calls in datacenters using hardware/software co-design
Keywords: Electrical Engineering.

In this dissertation, we present the nanoPU, a new NIC-CPU co-design that provides ultra low and predictable remote procedure call (RPC) response time and thus accelerates datacenter applications. The nanoPU achieves its goal by providing a fast path between the network and applications. This fast path has the following three characteristics: (1) it moves key resource scheduling decisions from software to hardware (reliable network transport & congestion control, RPC load balancing across cores, thread scheduling) allowing them to operate much more efficiently, (2) it provides a path directly between the network and applications which bypasses the cache and memory hierarchy placing arriving messages directly into the CPU register file, and (3) it supports a unique thread scheduling feature to bound the tail response time experienced by certain high-priority applications. We built an FPGA prototype of the nanoPU fast path by modifying an open-source RISC-V CPU, and evaluated its performance using cycle-accurate simulations on AWS FPGAs. The wire-to-wire time for nanoPU to receive an incoming message and initiate transmission of a response (response time) is just 69ns, an order of magnitude quicker than the best-of-breed, low latency, commercial NICs. Our hardware implementation of the NDP transport protocol adds less than 10ns to the wire-to-wire response time. We demonstrate that the hardware thread scheduler is able to lower (and potentially bound) RPC tail response time by about 5x while enabling the system to sustain 20% higher load, relative to traditional thread scheduling techniques. Furthermore, the nanoPU's core selection policy in hardware is able to efficiently distribute RPCs across cores eliminating hot spots and reducing tail response time. We implement and evaluate a suite of applications on the nanoPU, including MICA, Raft, and set algebra for document retrieval
Organizational Structure and Decision-Making in Corporate Venture Capital
Keywords: Department of Economics
corporate venture capital
corporate innovation
venture capital
deal structure
portfolio companies
balance sheet investing
Thesis

This paper studies corporate venture capital (CVC) units of large US corporations to learn how they make decisions across several areas: internal organization of CVC units, relationships with parent companies, CVC unit objectives, investment process and approval, deal structure, relationship with portfolio companies, compensation, and composition of CVC teams. The study is conducted by interviewing senior team members of seventy-four CVC units, representing 78% of the active CVC units of companies in the S&P 500 index. CVC units are organized in significantly more diverse ways than institutional VC firms. Unlike institutional VC firms, most CVC units do not manage committed venture funds, but instead invest from the balance sheets of their parent companies. Investment committees, in which parent company executives play a pivotal role in approving individual decisions, are common. Many corporate venture capitalists (CVCs) believe executives at their parent companies do not understand the norms of the venture space. The demographic composition of senior team members at CVC units is very different than that of their counterparts at institutional VC firms. The results raise a number of issues about the economic role of CVC units in corporate innovation.
Organized grouping mediates the environmental interactions of social squids
Keywords: Biology.

A surge of recent research suggests that current and future shifts in marine species' distributions are ultimately driven by physiological responses to environmental variation. These observations and predictions are important because changes in species' distributions cause ecological change through community reorganization. However, they do not necessarily capture the full effects of behavioral adaptations on species' ecologies. Behaviors mediate the response of an organism's physiological processes to environmental variation, and can profoundly affect individual fitness, and thus population demography and community dynamics, under environmental variability. One of the most conspicuous and taxonomically widespread behaviors of animal life in the ocean is social behavior, or aggregation behavior related to inter-individual cohesion among conspecifics. Social interactions can buffer physiological responses to stressful conditions, shape behavioral patterns of individuals, and drive individual decision-making, with ecological and evolutionary consequences. While these impacts are becoming increasingly apparent in other systems, to date very few investigations explicitly consider the mechanisms and physiological effects of social behavior in the ocean. Squid, a predominantly predatory clade of highly motile molluscs, are a particularly compelling group of marine animals with which to develop such understanding. Many squids are social, occur across a range of environmental conditions, possess activity-limiting physiological constraints, visually communicate with complex pigmentation patterning, and demonstrate considerable ecological and life history flexibility. In this dissertation, I examine the patterns, mechanisms, and physiological effects of social behavior in open ocean squids, and how physiological sensitivities to environmental variability in squids can lead to new ecological interactions of potential consequence. Joining an emerging body of research, my dissertation work shows how social behaviors that confer emergent benefits are adapted to environmental gradients in the ocean. It also highlights the importance of social behavior in understanding habitat selection and the corresponding ecological changes in the ocean. As empirical and theoretical research continues to explore how social behavior shapes such ecological patterns, the understanding we develop will facilitate more sustainable harvest, more effective conservation, and more accurate predications under global change