VictorJuiz/thesis huggingface dataset:
Data representation synthesis [electronic resource]

This dissertation introduces Data Representation Synthesis, a technique for specifying combinations of data structures with complex sharing in a manner that is both declarative and results in provably correct code. In our approach, abstract data types are specified using relational algebra and functional dependencies. We describe a language of decompositions that permit a programmer to specify different concrete representations for relations, and show that operations on concrete representations soundly implement their relational specification. Decompositions also give new insight into the problem of writing safe and efficient concurrent code. We introduce lock placements, which describe, for each heap location, which lock guards the location, and under what circumstances. By incorporating lock placements into decompositions, a compiler can automatically explore the space of possible legal data representations and locking strategies, while simultaneously guaranteeing correctness, serializability of relational operations, and deadlock-freedom. It is easy to incorporate data representations synthesized by our compiler into existing systems, leading to code that is simpler, correct by construction, and comparable in performance to the code it replaces. We describe our experience with a prototype compiler; code generated by our system can easily be dropped into existing systems in place of complex hand-written implementations with comparable performance.

Advisor: Aiken Alexander primary advisor. Thesis advisor
Dill David L. advisor. Thesis advisor
Fisher Kathleen
Data-driven methods for modeling healthcare risks [electronic resource] : insights and applications in drug surveillance and breast cancer incidence prediction

This dissertation develops data-driven methods for healthcare risk modeling to facilitate risk identification and evaluation, focusing on post-marketing drug safety surveillance and breast cancer incidence prediction. For risk identification, we first propose a novel methodology for post-marketing drug safety surveillance: "Follow the money'', which monitors cost in health insurance claims data to detect increased spending related to adverse drug events (ADE). By using real claims data from 2.4 million insured individuals, we demonstrate that this method enables early detection of signals of ADE. Second, we recommend statistical study designs to detect rare and time-dependent ADE resulting from long-term drug use and highlight statistical considerations that drug surveillance designers should focus on when implementing a long-term surveillance. We characterize statistical properties and compare performances of a variety of surveillance designs by applying them to simulated event data as well as real health insurance claims data. For risk evaluation, we develop a stochastic simulation model of the Surveillance, Epidemiology and End Results database to quantify the impact of menopausal hormone therapy (MHT) on U.S. breast cancer incidence. Our model quantifies the promoting effect of MHT on breast tumor growth and the reduced mammographic detectability. By modeling unobservable tumor natural history dynamics and simulating individual life history at a population level, we evaluate the contribution of MHT use cessation to the observed decline in breast cancer incidence between 2002 and 2003. We also predict U.S. breast cancer incidence in the absence of MHT.

Keywords: Management Science and Engineering
Advisor: Brandeau Margaret L. primary advisor. Thesis advisor
Bjarnadottir Margret V.
Ye Yinyu advisor. Thesis advisor
Zenios Stefanos A. advisor. Thesis advisor
De novo reconstruction of cell cycle and chemotherapeutic mechanisms in cancer [electronic resource]

Although cancer types vary widely, the number of new cancer drugs each year is severely limited. Even for those cancer therapies which are currently in use, prognostic outcomes vary significantly across cancer types. Drug discovery relies primarily on our knowledge of direct drug targets, but not the systematic off-target effects that these therapies may have. As a result, our knowledge of these drugs is somewhat limited to general mechanistic classes. Within these classes it is hard to find potential patient differences without time-intensive studies and trials. While drug classification relies on our knowledge of direct targets, it does not typically consider how a number of global cellular processes are ultimately affected. Quantifying the mechanistic differences between drugs is a difficult process. Current standards to quantify individual drug efficacy are large-scale measurements are taken at a heterogeneous population level, ignoring the effects of drug action or mechanism in single cells or cell populations. Because our knowledge is limited in this way, we are often surprised to find that similarly classified cancer drugs can have disparate effects in patients. Single-cell technologies including flow cytometry allow us to uncover relationships between drugs through simultaneous measurement of cell signal, cell cycle and cell type for each cell. Recent technological advances in flow cytometry have facilitated new clinical tests to determine cancer subtypes. In addition, these methodological advances have created potential for providing novel insights into drug mechanism and patient response. In this dissertation, I describe a new framework for performing mechanistic profiling of cancer cells. There are two facets of this problem. The first is an understanding of cancer cell cycle. Prior to treating with a drug, it is important to form a general model of how a cancer cell replicates. In a screening methodology, however, this is a difficult problem. I address this problem by building an automated, de novo model of cell cycle. Second, I perform cancer therapeutic profiling by measuring DNA damage, apoptosis, cell cycle, and cell signaling markers across multiple cancer cell types. In this thesis, I combine both cell cycle and drug profiling methods into a new drug profiling framework that can be used to find existing and novel cell cycle and drug-based biology. The results of our current work have major implications for use in profiling aberrant cell types in primary cancer samples, as well as mechanistic drug screening.

Advisor: Batzoglou Serafim primary advisor. Thesis advisor
Nolan Garry P. primary advisor. Thesis advisor
Altman Russ advisor. Thesis advisor
Butte Atul J. advisor. Thesis advisor
Deadline [electronic resource] : crime, journalism, and fearful citizenship in Caracas, Venezuela

This dissertation traces the constitutive relationship between the press and populism in Venezuela through an account of crime journalism. Based on more than two years of research alongside journalists on the Caracas crime beat, it analyzes how demands for security mobilized disparate sectors of society around a common cause. The vital link between the politics of security and populist mobilization is something that scholars have only recently begun to recognize (Bottoms 1995; Garland 2002; Pratt 2007; Wacquant 2009). Although my analysis focuses specifically on Venezuela, I believe that it has implications for studies of crime and for studies of populism more generally. This dissertation sets out three interrelated findings. First, crime news creates victims. I mean this in a discursive sense: crime news reproduces the figure of the innocent, wholesome victim. A glance at the morning papers or the midday news confirms as much. Crime journalism is replete with images of grieving relatives and stories about good kids, hard-working men, and beloved mothers who were murdered for no reason at all. Anthropologists studying mass media representations of crime have looked almost exclusively at the figure of the criminal. We know where the category of the criminal leads—to the body of the young, poor, dangerous, black male. What can we learn by following the category of the victim? Second, in Venezuela victimhood becomes legible through the discursive practice of denunciation. We tend to think of victims as silent, passive objects; yet in Venezuela victimhood is anything but silent. To the contrary, the victim is an active performer, someone who voices sentiments of rage, grief, tragedy, and fear. To denounce is to assert one's victimhood publicly, usually through the medium of the press. Third and finally, denunciations made in the name of victims are the building blocks that form populist movements. In Culture & Truth, anthropologist Renato Rosaldo talks about how finding the rage in grief helped him understand the impetus for Illongot headhunting (Rosaldo 1993). I argue that by seeing the rage in victimhood, we begin to understand the impetus behind populist mobilizations against violent crime. The discursive practices of denunciation on the part of victims, journalists, pundits, and media moguls allow us to analyze this process. Rather than reducing the phenomenon to a moral panic or a mob mentality, an analysis of denunciation actually shows us the mechanisms and the micro-politics through which the populist movements take shape.

Keywords: Anthropology.
Advisor: Yanagisako Sylvia Junko
Ebron Paulla A.
Ferguson James advisor. Thesis advisor
Karl Terry Lynn
Turner Fred advisor. Thesis advisor
Decomposition kinetics of the rocket propellant RP-1 and its chemical kinetic surrogates [electronic resource]

High-temperature fuel decomposition is an important aspect of fuel chemistry, and a thorough understanding of this process is necessary in order to accurately describe combustion chemistry. Two major motivations drive the study of kerosene decomposition. First, it is a vital building block upon which oxidation chemistry can be built, and second, it is used as a coolant in many rockets and high-speed aircraft. Measurements of fuel and ethylene time histories during decomposition of RP-fuels and their possible surrogate components were carried out between 1000 and 1500 K in two shock tubes. From these measurements, overall fuel decomposition rates for six fuels (RP-1, RP-2, JP-7, n-dodecane, methylcyclohexane, and iso-cetane) were determined and ethylene yields for four of these fuels (RP-1, n-dodecane, methylcyclohexane, and iso-cetane) were also determined. An RP-1 decomposition surrogate was formulated based on three targets: compound class, overall fuel decomposition rate, and ethylene yield. This resulted in a surrogate containing 32% n-dodecane, 59% methylcyclohexane, and 9% iso-cetane.

Keywords: Mechanical Engineering
Advisor: Hanson Ronald primary advisor. Thesis advisor
Bowman Craig T. (Craig Thomas)
Mitchell Reginald advisor. Thesis advisor
Deconstructing the cerebellar learning algorithm [electronic resource]

This thesis is composed of experimental and analytic work deconstructing the cerebellar learning algorithm. The central finding is that two parallel neural circuits, with independent instructive signals, support cerebellum-dependent learning. An analytic framework for this result, based on overcoming signal-dependent noise, is proposed.

Advisor: Raymond Jennifer L. primary advisor. Thesis advisor
Clandinin Thomas R. (Thomas Robert)
Greicius Michael D. advisor. Thesis advisor
Moore Tirin
DeduceIt: A Tool for Representing and Evaluating Student Derivations

Almost all automated online grading systems today are currently limited to multiple-choice problems or fill-in-the-blank answers. DeduceIt is a system directed at university-level math and engineering education that aims to allow students to enter substantial formal derivations which are then automatically checked. Current theorem-proving technologies are generally intended to support one expert in writing a potentially long and complex derivation; online education, however, requires a system that can be used without expertise in a field, and that can be effectively used by tens of thousands of students attempting to do the same exercise in many different ways. While DeduceIt borrows heavily from existing theorem-providing techniques, the special constraints of online education lead to unique challenges and a substantially different overall design.

Keywords: DeduceIt
theorem-proving technologies
Thesis
Advisor: Stanford University Program in Engineering Physics
Koller Daphne
Aiken Alex
Defining novel functions for the oncogenic variant EGFRvIII in tumor initiation [electronic resource]

Glioblastoma multiforme (GBM) is the most common and deadly primary brain tumor. Amplification and rearrangements of the epidermal growth factor receptor (EGFR) gene are frequently found in GBM, and the most common variant is EGFRvIII. Here we report that EGFRvIII is a novel cancer stem cell (CSC) or tumor initiating cell marker in GBM. EGFRvIII expression is restricted to sub-regions and sporadic cells within the tumor and EGFRvIII is frequently co-expressed in the putative CSC CD133+ population. EGFRvIII gene and protein expression can be maintained when primary GBM are cultured as tumor derived neurospheres and is lost when spheres are exposed to differentiating conditions. Furthermore, cells that express both EGFRvIII and CD133 display the greatest self-renewal and tumor initiating abilities when compared to the single positive or double negative populations. If amplification and rearrangement are early events in tumorigenesis, this implies that they should be preserved across the tumor despite focal expression. Unexpectedly, we found EGFR amplification and rearrangement throughout the tumor, including regions with no EGFRvIII expression. To study this phenomenon, we characterized three GBM cell lines with endogenous EGFRvIII expression. EGFRvIII defined a hierarchy and epigenetic mechanisms played a role in maintaining heterogeneous EGFRvIII expression, suggesting that drugs which modulate the epigenome might be used successfully in glioblastoma tumors. To assess a broad applicability of our findings to other tumor types, we investigated EGFRvIII expression and amplification in primary breast carcinoma. Our analyses confirmed the presence of EGFRvIII, but in the absence of amplification or rearrangement of the EGFR locus. Interestingly, EGFRvIII was often expressed in a subset of tumor cells and was co-expressed in the putative CSC CD44+/CD24-/low population. EGFRvIII-expressing cells had increased expression of stem cell and epithelial-mesenchymal transition (EMT) associated genes and increased in vitro sphere formation and in vivo tumor formation compared to non-expressing cells. Mechanistically, EGFRvIII mediated its effects through the Wnt/[beta]-catenin pathway. Collectively, this data suggests a novel function for EGFRvIII in tumorigenesis and further supports the use of EGFRvIII-targeted therapy in these tumor types.

Keywords: Cancer Biology.
Advisor: Wong Albert J. primary advisor. Thesis advisor
Clarke Michael F.
Cleary Michael L. advisor. Thesis advisor
Palmer Theo advisor. Thesis advisor
Wernig Marius advisor. Thesis advisor
Demystifying unsupervised feature learning [electronic resource]

Machine learning is a key component of state-of-the-art systems in many application domains. Applied to many kinds of raw data, however, most learning algorithms are unable to make good predictions. In order to succeed, most learning algorithms are applied instead to "features" that represent higher-level concepts extracted from the raw data. These features, developed by expert practitioners in each field, encode important prior knowledge about the task that the learning algorithm would be unable to discover on its own from (often limited) labeled training examples. Unfortunately, engineering good feature representations for new applications is extremely difficult. For the most challenging applications in AI, like computer vision, the search for good features and higher-level image representations is vast and ongoing. In this work we study a class of algorithms that attempt to learn feature representations automatically from unlabeled data that is often easy to obtain in large quantities. Though many such algorithms have been proposed and have achieved high marks on benchmark tasks, it has not been fully understood what causes some algorithms to perform well and others to perform poorly. It has thus been difficult to identify any key directions in which the algorithms might be improved in order to significantly advance the state of the art. To address this issue, we will present results from an in-depth scientific study of a variety of factors that can affect the performance of feature-learning algorithms. Through a detailed analysis, a surprising picture emerges: we find that many schemes succeed or fail as a result of a few (easily overlooked) factors that are often orthogonal to the particular learning methods involved. In fact, by focusing solely on these factors it is possible to achieve state-of-the-art performance on common benchmarks using quite simple algorithms. More importantly, however, a main contribution of this line of research has been to identify very simple yet highly scalable feature learning methods that, by virtue of focusing on the most critical properties identified in our study, are highly successful in many settings: the proposed algorithms consistently achieve top performance on benchmarks, have been successfully deployed in realistic computer vision applications, and are even capable of discovering high-level concepts like object classes without any supervision.

Advisor: Ng Andrew Y.
Koller Daphne advisor. Thesis advisor
Li Fei Fei
Depositional architecture of deep-water slope systems [electronic resource] : examples from the quaternary Lucia Chica channel system, offshore central California and the upper Miocene Urenui formation, New Zealand

Deep-water channels are submarine conduits that transport subaerial and shallow marine sediments into the deep-sea. Much of the sediment transport and deposition through deep-water slope channels occurs as turbidity currents that are recorded in deposits, termed turbidites. I utlize outcrop and seafloor turbidite datasets to investigate deep-water channel formation and fill in two different slope depositional systems--the Quaternary Lucia Chica channel system, offshore central California, and the upper Miocene Urenui Formation, New Zealand. Extensive exposures of the Urenui Formation along the northern Taranaki coast are used to investigate channel-fill architecture and influences on slope sedimentation in ancient mid- to uppermost-slope channels. Variations within sandstone and conglomerate lithofacies are interpreted to reflect relative position of channels on the paleo-slope. I generated a composite architecture at lithofacies-scale from the truncated Taranaki outcrops that suggests conduit development, phases of channelization, and channel abandonment within the slope channels. In contrast to the Urenui Formation outcrops, the Lucia Chica includes a mid-slope avulsion belt with four adjacent channels and a complicated pattern of scours imaged with Autonomous Underwater Vehicle high-resolution seafloor and outcrop-scale subsurface data. This extremely high-resolution imaging leads to new interpretations of avulsion sequence, distribution of turbidity currents, and channel development. The interpreted sequence of channel development involves erosional channel inception through scouring and incipient channels (defined by linear trains of scours) prior to development of continuous thalwegs. Channel narrowing, formation and growth of levees, increasing channel relief, and development of sinuosity occurred as channels evolved. These results challenge existing conceptual models of channel continuity, fluvial analogs, and channel inception, migration, and abandonment developed from other deep-water systems with lower-resolution imaging. Implications of this research extend to academic studies of deep-water channel development, reservoir-scale analogs of stacking patterns relevant to petroleum applications, and exploration of the modern seafloor.

Keywords: Geological and Environmental Sciences.
Advisor: Graham S. A. (Stephan Alan)
Fildani Andrea advisor. Thesis advisor
Hilley George E. advisor. Thesis advisor
Lowe Donald R.
Paull Charles K. advisor. Thesis advisor
Design and fabrication of nanostructures for light manipulation in solar cells and microelectromechanical systems [electronic resource]

This work is about nano-structuring of semiconductor devices for the improvement of their optical properties. The phenomenon of guided resonances in photonic crystal (PC) slabs will be introduced and ways of engineering these guided resonances for a variety of applications such as improved reflectivity in micro-electro-mechanical (MEMS) mirrors and increased absorption in thin-film solar cells will be discussed. The first part of the work focuses on the application of PCs in MEMS. A new process (GOPHER) that was developed to make low stress PCs out of monolithic silicon will be discussed. The advantage and ease of making multilayer PCs with Gopher will be illustrated and experimental results showing Gopher structures having spectra with broadband reflectivity (suitable for mirror applications) as well as sharp peaks (suitable for sensors) will be presented. Other applications of Gopher such as making waveguides etc., will be briefly discussed. The integration of a high quality PC mirror with a 1D resonant MEMS scanner will be demonstrated. Finally, the results of fabrication of a prototype wafer-scale encapsulated optical device will be shown. The Gopher process has a lot of potential for facilitating the integration of micro- and nano-scale photonics with CMOS circuitry. The second part of the work focuses on the application of PCs for light-trapping in solar cells. Thin-film photovoltaics has the potential to reduce cost by reducing the amount of photoactive material required and allowing for the use of material of poorer quality. Crystalline Silicon (c-Si) is an attractive material for photovoltaic cells due to its natural abundance, nearly ideal band gap, and leverage of existing process and materials knowledge. However, the poor optical absorption in the near-infrared spectral range requires the use of very efficient light trapping techniques. One such technique that is explored is to pattern the active layer into a 2D PC. Electromagnetic simulations are performed to show that an enhancement in integrated short-circuit current by a factor of 3 is possible when compared to a planar slab of equivalent volume. This is because the PC supports guided resonances into which incident radiation can couple which increases the absorption. Finally, the fabrication of an ultrathin c-Si solar cell where the active material is patterned into a square-lattice 2D PC is demonstrated. Both short-circuit current and external quantum efficiency measurements show an enhancement in optical absorption, especially at longer wavelengths. Scanning photocurrent maps confirm the improved optical absorption in the photonic structure over an unpatterned reference. Future applications of nanostructuring to thin-film cells that can be commercially realized is discussed.

Keywords: Applied Physics
Advisor: Brongersma Mark L. primary advisor. Thesis advisor
Byer R. L. (Robert L.)
Solgaard Olav advisor. Thesis advisor