VictorJuiz/thesis huggingface dataset:
Is Silent R&D Productive?

In competitive markets where ideas are getting harder to find, firms have strong incentives to keep their ideas quiet. For this reason, many innovative firms do not disclose research and development (R&D) expenses even if their research efforts are very productive. I identify “silent R&D” firms as firms that operate in an innovative industry and choose not to disclose R&D as a separate line item but instead pool R&D with sales, general, and administrative (SG&A) expenses. I document that there is a significant proportion of silent R&D firms in the sample of the U.S.-listed stocks and that this proportion is stable over time at 13% on average. I show that silent R&D firms are different from missing R&D or trade secrecy firms identified in prior research and are not associated with poor disclosure quality. Finally, I find that silent R&D firms have higher future profitability at the magnitude of about 3 to 4.5 percent accumulated over the next three years. This higher future profitability is primarily due to the higher productivity of their intangible capital. This evidence suggests strongly that silence enshrouds better ideas. Using the findings from this paper, I also provide initial estimates of SG&A reclassification rates for silent R&D firms.

Advisor: Patatoukas, Panos N.
Printed Nitrate Sensor Nodes for Precision Agriculture

Nitrate is a critical nutrient for plant growth and a primary component of commercialfertilizers. Unfortunately, nitrate production is an energy-intensive process and, when applied in excess, generates greenhouse gases and pollutes the water supply. Despite this, tools for monitoring nitrate and other nutrients in the soil are inadequate. Measurements must be made at a high spatial and temporal resolution to optimize agricultural water and fertilizer inputs./nWe will present the model-based design, fabrication, and implementation of wirelessnitrate sensor nodes in a precision farming system. First, I will discuss the design, fabrication, and characterization of fully printed potentiometric nitrate sensors, describing in detail key performance metrics and benchmarking our sensors against existing sensors in the literature. We will next dene and demonstrate the optimized placement of soil sensors in agricultural fields using machine learning optimization approaches to determine the scale of production required to meet the project goals and inform the design space of the wireless sensor nodes. The nitrate sensors were modied, integrated into a WiFi-enabled sensor node, and characterized in varying solution and soil conditions. The sensor node was adapted into a low-cost, naturallydegradable, passive RFID nitrate sensor node, characterized, and modeled into a wireless sensor network sampled by autonomous UAV drones. Finally, I describe the challenges of interference and present preliminary results of a multianalyte nitrogen sensor array.

Advisor: Zohdi, Tarek
Arias, Ana C
Contextual Spatial Computing: A Generative Approach

Spatial Computing interfaces such as augmented reality (AR), virtual reality (VR), and mixed reality (MR) have become promising modalities for next-generation computing platforms. Along with its potential impact on various technological applications, spatial computing comes with spatial limitations itself. Such experiences are physically constrained by the geometry and semantics of the local user's environment where existing physical elements are present. Unlike 2D screens, where a rectangular screen region can host digital content with possible overlay, 3D environments are occupied with diverse physical obstacles and functional constraints. This results in complex and, many times, non-convex activity spaces available for virtual content augmentation. Target environments are not necessarily known to content developers, and hence the ability to deploy large-scale curated experiences that can adapt to a diverse set of user spaces is challenging. This limitation is elevated in remote telepresence scenarios, where identifying a common ground physically accessible for all participants can become difficult, especially if users are unaware of the spatial layout of other participants' physical environments./nMotivated by these spatial challenges, this dissertation works towards developing context-aware generative frameworks which enable large-scale deployment of adaptable spatial computing experiences for everyday users in diverse target environments. By introducing novel workflows to learn from examples as priors and utilizing spatial optimization methods, the systems developed in this dissertation address the spatial challenges in spatial computing in various applications of remote workplaces, multi-user telepresence, and curated experiences such as games and education. The contributions of this dissertation consist of solving two general sets of problems: 1) developing curated context-aware spatial experiences for large-scale deployment, which include a wide variety of diverse target spaces not known to the content developer; and 2) facilitating telepresence experiences, where participants are not aware of each other's local spaces due to the Telepresence Spatial Mapping Problem (TSPM), explained in this dissertation. The frameworks developed in this work can play a role in increasing the adoption of spatial computing interfaces in everyday environments, allowing developers to design and curate content in scale without knowing the target scene of the user itself. Moreover, the frameworks proposed here can potentially facilitate remote workplace practices and virtual collaborations by decreasing the spatial requirements for telepresence systems.

Advisor: Caldas, Luisa
Engaging Indigenous Political Theories: Colonial Histories, Decolonial Futures, and Indigenous Political Thought in British Columbia, Canada

Despite wider efforts to deparochialize and decolonize political theory, few contemporary political theorists have seriously engaged the diverse political theories of Indigenous peoples and the challenges Indigenous thinkers have posed to dominant political theory discourses. This dissertation considers what changes might be needed for Indigenous political thought and Indigenous ways of knowing to gain a meaningful presence in the subfield of political theory, and in particular, how engaging Indigenous political thought might support this presence. Building on the relevant work of both Indigenous and non-Indigenous scholars across disciplines, the dissertation suggests that engagements with Indigenous political thought take up practices of reflexivity attentive to the relational and political embeddedness of theorizing, of respectful relationship building, and of prioritizing Indigenous political goals in the theorizing or research process. In a reflexive analysis of the discipline of political theory, the dissertation considers how political theorists might understand the role of the subfield in perpetuating or reinforcing the marginalization of Indigenous people and their political thought. Through historical analysis, it is demonstrated that vestiges of the developmental historicist tradition, and in particular, assumptions about the nation-state as a natural unit of political organization and about the supposed apolitical nature of Indigenous cultures, help to explain many misrepresentations of Indigenous peoples. Next, the dissertation turns to an engagement with previously unstudied Indigenous political thought from British Columbia, Canada, illustrating the other suggestions for engaging Indigenous political thought. Historically narrating and analyzing seven decades of the history of Indigenous political thought in British Columbia, it is demonstrated that Indigenous peoples from the region have longstanding traditions of theorizing territory and political authority that challenge the legitimacy of the authority of the settler-state. Next, using collaborative methods, an interpretive account and analysis of the contemporary political thought of a hereditary chief from the shíshálh Nation is offered. The dissertation makes clear that questions of territoriality and authority have never been settled in Canada, and in British Columbia in particular, and offers guidance for those considering what it might take to build a meaningful presence of Indigenous knowledge and Indigenous people within political theory.

Advisor: Ray, Isha
O'Neill, Kate
On the Influence of Superhydrophobic Coatings on Turbulent Boundary Layer Separation

This experimental study of turbulent boundary layer separation from a contoured backward facingramp demonstrates that a spray-on superhydrophobic coating modifies the location of turbulent boundary layer separation. A custom water tunnel was designed and constructed to study turbulent boundary layer separation from a backward facing ramp located downstream of a flat plate. Particle image velocimetry was used to measure the velocity field and identify the onset of turbulent boundary layer separation. The flow rate within the water tunnel was adjusted to study the separation of turbulent boundary layers at Reynolds numbers based on the plate length of 500,000 to 6,000,000./nExperiments on a smooth baseline surface showed that the turbulent boundary layer remained fullyattached along the ramp at low flow rates. The boundary layer detached as the fluid velocity was increased, and the location of the onset of separation advanced upstream with increasing velocity. The application of a superhydrophobic coating caused the onset of separation to occur at a lower flow rate, as well as the location of separation to advance further upstream at a fixed flow rate. Analysis of the velocities in the turbulent boundary layer upstream of the ramp show that the separation location is modified due to a decrease in the Reynolds shear stress within the boundary layer. This decrease in turbulent mixing, brought about by the presence of a superhydrophobic coating, reduces the transport of momentum to the wall which leads to earlier onset of turbulent boundary layer separation. This identifies a concern that should be addressed as superhydrophobic coatings are investigated as a method for reducing ship resistance within the maritime industry.

Advisor: Mäkiharju, Simo A
Savaş, Ömer
Molecular MRI Approaches for Noninvasively Characterizing Renal Pathophysiology

Renal diseases that involve filtration function loss are usually diagnosed through measuring estimated glomerular filtration rate (eGFR) via blood tests such as blood urea nitrogen and serum creatinine level measurements. Although these methods allow quick assessment of renal function, it does not tell the cause of function loss and lacks adequate sensitivity due to the hyperfiltration by intact nephrons. These limitations become more critical in cases that involve progressive renal function loss such as acute kidney injury (AKI)-to-chronic kidney disease (CKD) transition and chronic graft function loss after renal transplantation, in which microstructural alterations often precede the change in blood marker levels. Biopsy remains the gold standard to pick up these tissue level damages, but the procedure is invasive and prone to sampling errors. Here, the use of novel molecular magnetic resonance imaging (MRI) technique called chemical exchange saturation transfer (CEST) imaging is demonstrated for noninvasively characterizing renal pathophysiology. Specifically, urea was studied as an imaging target and contrast agent of CEST MRI, as urea is a major metabolite responsible for controlling water reabsorption in the kidneys. Characterization of urea showed concentration and pH-dependent CEST contrast, and the feasibility of in vivo urea imaging was demonstrated with heightened CEST contrast in the inner medulla and papilla of the mouse kidneys after urea infusion. This method was further optimized for better quantification of urea by choosing the optimal time point of acquiring CEST data after urea infusion, removing T1 time effect on the CEST contrast and adopting multi-pool Lorentzian fitting. Lastly, this technique was applied to mouse renal disease models to test its diagnostic potential. Acute and chronic nephropathies were induced in mice, and the CEST data were longitudinally acquired along the disease progression. Since CEST data also reflects the presence of other mobile proteins, metabolites, and semi-solid macromolecules, the CEST contrast from these molecules were also analyzed in addition to urea. The urea CEST showed significant changes upon acute injury development, and the multiparametric CEST clearly distinguished acute and chronic nephropathies. Overall, CEST MRI can observe urea recycling in the mouse kidneys, and allows distinguishing different nephropathies along with other endogenous CEST contrast.

Advisor: Vandsburger, Moriel H
Efficient solvers for the implicit time integration of matrix-free high-order methods

In this thesis, we develop and study efficient solvers for high-order Galerkin methods applied to fluid flow problems.Many flow problems necessitate implicit time-integration schemes in order to be practical. Implicit-in-time discretizations require the solution of nonlinear algebraic systems each time step, which are often in-turn solved by linear solvers. Therefore, the performance of implicit-in-time solvers is largely determined by the performance of the underlying linear solvers./nOne approach to create efficient methods is to work with matrix-free operators.Because assembling the underlying discretization matrix can be prohibitively expensive in terms of computational complexity and memory, matrix-free operators are an attractive alternative. These operators replace the matrix-vector products with on-the-fly sum-factorization evaluations of the discretized differential operators instead. Indeed, their high arithmetic intensity makes these operators particularly well suited for modern graphics processing units (GPU) and GPU-accelerated architectures./nThese matrix-free operators are particularly challenging to precondition, however, because they by design do not allow access to the underlying matrix entries.We create a suite of efficient matrix-free preconditioners for a range of fluid flow problems that are robust with respect to polynomial degree and mesh size. The main building block solver extends sparse, low-order refined preconditioners with parallel subspace corrections. This work tackles Poisson problems, saddle-point Stokes systems, and the incompressible Navier-Stokes equations in two and three spatial dimensions./nA different set of problems exhibit geometrically localized stiffness, where convergence rates are degraded in a localized subregion of the mesh.Generic preconditioners do not perform well across the entire domain because of mesh size, mesh anisotropy, highly variable coefficients, or more challenging physics in the subregion. Therefore, we seek to save costs by utilizing cheap preconditioners for most of the mesh and only focus our effort on the less expensive subregion problem. Our iterative subregion correction preconditioners correct naive preconditioners with an adaptive inner subregion iteration to reduce the number of costly global iterations. This work demonstrates performance on basic convection-diffusion problems, high Reynolds number compressible flow problems, and a $30^\circ$ angle of attack problem with massively separated flow.

Advisor: Persson, Per-Olof
Generic architectures for efficient Hyper-Dimensional Computing

The last decade has witnessed a slowdown in technology scaling. At the same time, the emergence of machine learning has substantially increased computational demand. While these trends have seriously challenged traditional paradigms for digital design, novel computing methods based on randomness can be leveraged for continued increase in performance and energy efficiency. Hyper-dimensional Computing (HDC), a brain-inspired paradigm using high-dimensional random vectors as its fundamental data-type, shows promise. It is known to provide competitive accuracy on sequential prediction tasks with far smaller model size and training time compared to conventional machine learning, and is robust to representation errors. This dissertation considers efficient architectures for real-time Hyper-dimensional Computing on edge devices. After a study of implementations on classical compute platforms, a highly-pipelined, data-flow architecture is developed from the first principles of HDC. A detailed construction for a reasonably large HDC processor, capable of supporting a wide variety of prominent body-sensing applications, forms the foundation of this work. The basic architecture is extended to HDC processors requiring more than a single bit of representation for vector elements. Using results from high-dimensional probability theory, a numerical normalization is proposed and its effectiveness is proven for applications obeying reasonable assumptions on vector elements’ distribution. Verification experiments indicate that empirical performance of the proposed normalization is far better than the theoretical guarantee. A 2048-bit wide HDC processor, designed using the architectural principles developed here, was manufactured in a leading technology node. The chip reliably achieves an energy cost of approximately 30 nanojoules per classification on a state-of-the-art body-sensing application – the best when compared to all known previous hardware in the existing literature. Measurements establish energy efficiency and robustness of the designed processor. The architecture, arguments, experiments and measurements presented in this dissertation confirm the great potential of Hyper-Dimensional Computing as a computing paradigm capable of competitive performance in extremely energy-constrained environments.

Advisor: Rabaey, Jan
Multiple Modes of PV Interneuron Plasticity in Mouse Somatosensory Cortex

The cerebral cortex continuously adapts through learning and changes in sensory experience. A major goal of neuroscience is to understand the cellular and synaptic plasticity mechanisms that allow behavior to adapt to a changing environment. Remarkably, neural circuits maintain stable activity despite ongoing adaptive changes in synaptic strength and connectivity. Recent work has revealed multiple homeostatic plasticity mechanisms that maintain neural firing rates within an optimum range. However, it is not well understood how brain circuits maintain homeostasis in different brain regions and across different time scales./nIn primary sensory cortex, changes in sensory input drive adaptive changes in cortical representations. Broadly, depriving a set of sensory inputs rapidly weakens neural responses to those inputs and gradually strengthens spared inputs. Recent studies show that deprivation also triggers rapid plasticity in inhibitory circuits that could stabilize neural activity despite ongoing weakening of deprived inputs. Chapter 2 investigates the cellular and circuit mechanisms that underlie this rapid plasticity of inhibition in the superficial layers of rodent primary somatosensory cortex (S1). This study shows that 1-day whisker deprivation weakens inhibition in layer (L) 2/3 pyramidal neurons by decreasing the intrinsic excitability of L2/3 parvalbumin-positive (PV) interneurons near spike threshold. Deprivation reduces PV spike threshold through an increase in voltage-gated potassium conductances. These findings demonstrate that activity in sensory cortex is rapidly stabilized through plasticity of PV intrinsic excitability./nOften, a single sensory manipulation drives multiple cellular and circuit changes with distinct temporal components. In cortical inhibitory circuits, the time course of plasticity is not well understood. Building on the studies in Chapter 2, we hypothesized that additional plasticity mechanisms may be recruited in L2/3 PV neurons in response to different time scales of whisker deprivation. In Chapter 3, we test this idea by extending the the duration of whisker deprivation to 3 days and asking whether changes in L2/3 PV circuits are consistent with those observed after 1 day of deprivation. We find that 3-day deprivation also decreases PV intrinsic excitability, but through a different mechanism than 1-day deprivation. Deprivation strengthens the medium afterhyperpolarization (mAHP) without affecting spike threshold. Thus, experience-dependent plasticity of cortical PV circuits involves a succession of distinct components.

Advisor: Feldman, Daniel E
Decentralized Ledgers: Design and Applications

A vast majority of online services nowadays are built atop centralized systems, in which data is stored and managed by a trusted party. However, centralization brings significant drawbacks, such as a central point of attack, poor transparency, and poor auditability. Recent success in the field of blockchain has led to great interest in secure decentralized ledgers because they enable a set of heterogeneous parties to reach a consensus on the validity of data on ledgers without relying on centralized service providers. In addition, decentralized ledgers often promise a foundation of decentralized trust and auditability for applications. Unfortunately, the benefits of decentralized ledgers often come at the expense of privacy and efficiency.In this dissertation, I will present my work on secure and efficient decentralized ledgers. I will show how to improve the efficiency and privacy of decentralized ledgers, which involves the design of new cryptographic tools such as zero-knowledge proofs and authenticated data structures. I will also show how to leverage decentralized ledgers to build practical and secure systems for real-world applications such as the Internet of Things and file sharing services.

Advisor: Popa, Raluca
Chiesa, Alessandro
Strategies in Complex Molecule Synthesis Enabled by Scaffold Remodeling and Late-Stage C–H Functionalization

One of the many goals of natural product total synthesis, is to leverage modern chemicalmethods to develop strategic paradigms which result in increasingly efficient synthesis routes. The work described in this dissertation explores the development of strategies enabled by skeletal remodeling tactics and C–H functionalization. Chapters 1 and 2 provide background for the work discussed in the subsequent chapters. Chapter 1 discusses the development of skeletal remodeling tactics by the Sarpong Group. Chapter 2 introduces a theoretical framework for application of C–H oxidation in complex molecule synthesis. It utilizes that framework in comparative analysis of previous syntheses which have employed C–H oxidation reactions, to highlight the strategies which they enable. Chapter 3 discusses our progress toward a total synthesis of the diterpenoid natural product sordaricin. An efficient synthesis of sordaricin might enable the development of novel an- tifungal lead compounds. We proposed the use of a “camphor-first” strategy to synthesize sordaricin. This strategy would utilize several C–H oxidations to enable construction of the core around a [2.2.1]bicyclic fragment, an approach distinct from previous strategies. Several potential routes are discussed which culminated in the successful synthesis of intermediates in which the western portion of the molecule. Chapters 4 and 5 discuss our synthetic studies of the longiborneol family of sesquiterpenoids. Chapter 4 discusses how a “camphor-first” strategy, similar to that proposed for sordaricin, enabled a succinct synthesis of the parent natural product, longiborneol. Chapter 5 discusses how we extensively employed C–H oxidation chemistry to develop divergent syntheses of nine additional natural products. Overall, this work demonstrates that strategies utilizing both skeletal remodeling tactics and C–H oxidation can enable exceedingly efficient syntheses, and may have further merit in complex molecule synthesis.

Advisor: Sarpong, Richmond