VictorJuiz/thesis huggingface dataset:
The physiological consequences of altering genetic controls in stomatal development of Arabidopsis thaliana [electronic resource]
Keywords: Biology.

Stomata are pores on the epidermis of plants that facilitate the exchange of gases between the interior of the plant and the external environment. Gas exchange is a fundamental process for plants, as it permits atmospheric carbon dioxide to perfuse into cell tissues that perform photosynthesis, but as a corollary, it also releases water vapor. The consequences of these activities extend far beyond the productivity of an individual plant, as transpiration and carbon sequestration have dramatic effects on ecosystem structure, global energy cycles, and are even capable of driving large shifts in climate. Here, genetic tools in Arabidopsis thaliana were used to alter the density and pattern of stomata on the epidermis and the physiological outcomes of those changes were quantified by leaf-level gas exchange experiments, measurements of water-use efficiency, microscopic analysis of leaf morphology, and predictive modeling. Changes in stomatal development had significant impacts on operational stomatal conductance -- the rate of gas exchange -- and net carbon assimilation. Stomatal conductance could be modeled as a function of stomatal density and size (gsmax) and the physiological responses to increasing ambient carbon dioxide were attributed to pore-specific properties of stomata. Furthermore, genetic regulators of stomatal development were capable of driving organizational changes in mesophyll tissues of the leaf interior, thus altering photosynthetic potential. These results indicate that stomatal development plays a critical role in structuring the physiological capacity of the leaf and stomatal anatomy can be characterized to predict plant-environment interactions under past, current, and future regimes of atmospheric carbon dioxide.
Platelet dynamics in whole blood and interpreting the interfacial stress rheometer [electronic resource]
Keywords: Chemical Engineering.

Complex interfaces have an important and sometimes subtle role to play in a number of fluid mechanical contexts, and this thesis is an investigation of two such situations. In the first case, an investigation of particle migration in blood flow, the complex interface is the red blood cell membrane, which has an important effect on both bulk flow properties as well as particle dynamics within blood vessels. The second case focuses on the direct measurement of complex interfacial properties using the interfacial stress rheometer, or ISR.
The poetics of dehumanization in modern Jewish literature [electronic resource]
Keywords: Comparative Literature.

This dissertation is a comparative study of the trope of dehumanization in some of the major works of modern Jewish literature written between 1851 and 1929. Jewish writers such as Heinrich Heine, Sholem Yankev Abramovitsh, Chayim Nachman Bialik, Uri Zvi Greenberg, and Franz Kafka found in a dehumanized, bestialized form a universal allegorical figure that captures the place of the Jew as an outcast from human society. The various instances in which Jews are transformed into animals — into a mare in Abramovitsh's story Di Kliatsche; or into fleas and spiders in Bialik's Be'ir Haharegah; or into a "monstrous vermin" in the case of Kafka's Gregor Samsa — expose a state of abandonment to earthly suffering, devoid of redemption, shared by humans and animals. The similarities between Jews and animals in these cases are not restricted to a metaphorical level, but instead reveal an essential aspect of the modern Jewish condition. By consistently depicting a borderline state between humanity and animality, these literary examples allow us to imagine the lived experience of Jews in the modern world before World War II.
A positron emission tomography (PET) neuro insert for combined PET/magnetic resonance imaging [electronic resource]
Keywords: Bioengineering.

The simultaneous acquisition of PET and MRI data shows promise to provide powerful capabilities to study disease processes in human subjects, guide the development of novel treatments, and monitor therapy response and disease progression. A brain-size PET detector ring insert for an MRI system is being developed that, if successful, can be inserted into any existing MRI system to enable simultaneous PET and MRI images of the brain to be acquired without mutual interference. The PET insert uses electro-optical coupling to relay all the signals from the PET detectors out of the MRI system using analog modulated lasers coupled to fiber optics. Because the fibers use light instead of electrical signals, the PET detector can be decoupled from the MRI making it partially transparent to the RF field of the MRI. Also, the number of laser-fiber channels in the system was reduced using techniques adapted from the field of compressed sensing. Using the fact that incoming PET data is sparse in time and space, electronic circuits implementing constant weight codes uniquely encode the detector signals in order to reduce the number of electro-optical readout channels by greater than 8-fold. Two out of a total of sixteen electro-optical detector modules have been built and tested with the entire RF-shielded detector gantry for the PET ring insert. The two detectors have been tested outside and inside of a 3T MRI system to study mutual interference effects and simultaneous performance with MRI. Preliminary results show that the PET insert is feasible for high resolution simultaneous PET/MRI imaging for applications in the brain.
Positron emission tomography (PET)-guided dynamic tumor tracking for radiation therapy [electronic resource]
Keywords: Electrical Engineering.

External beam radiation therapy plays an important role in the treatment of lung cancers. The success of this treatment depends on motion management, as lung tumors move during treatment due to respiration. The ultimate strategy for motion management is dynamic tumor tracking by anatomical imaging systems such as X-ray, ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI). However, positron emission tomography (PET), a functional imaging system, has not been incorporated with radiotherapy systems due to the limitations of PET in spite of higher sensitivity and specificity of PET for lung cancer. In the first phase of the dissertation, an investigation was performed into the potential and feasibility of PET for dynamic lung tumor tracking. We developed a tracking algorithm and focused on evaluating the accuracy of the algorithm, simulating real-time applications in a dynamic phantom study. In a commercial PET scanner, list-mode PET data were acquired from a phantom programmed to move with tumor and respiratory motion traces measured during radiotherapy. The results give an estimate of the real-world accuracy of the algorithm, showing an overall time-averaged error of approxi-mately 2 mm. The dynamic PET tumor targeting method was applied to the list mode PET data of lung cancer patients, demonstrating clinical feasibility of real-time tumor tracking. To maximize PET-tracking sensitivity in the presence of motion, it is important to maintain breathing regularity, which links to the second phase of this dissertation. In the second phase of the dissertation, we demonstrated the impact of audiovisual (AV) biofeedback on PET. AV biofeedback, a respiratory training system for patients, has been demonstrated to improve breathing regularity in the previous studies. In a dynamic phantom study, AV biofeedback significantly reduced motion blurring artifacts, but in a 5-patient study, AV biofeedback did not demonstrate statistically significant results. The findings from the patient study will be used to optimize the human-computer interface and include patient training sessions for improved comprehension and capability.
Pragmatics and the social meaning of determiners [electronic resource]
Keywords: Linguistics.

Language users draw all kinds of inferences concerning the opinions, moods, backgrounds, and social relations of speakers on the basis of what they say, and much of what is conveyed depends only indirectly, if at all, on the literal content of what is said. Though meaning beyond the literal comprises a hefty and potent share of linguistic meaning--as recognized in the traditions of both Gricean pragmatics and meaning-based sociolinguistics--much remains to be uncovered and explained as regards this domain. In this work, I develop a socio-pragmatic framework for understanding meaning beyond the literal, with an eye toward social meaning in particular, and with definite referential phrases as my empirical focus. I apply the framework in two case studies of social meaning, examining the definite article 'the' and demonstratives. In the first case study, I show that, in referring to a group of people, using 'the' ('the Americans') as opposed to a bare plural ('Americans') tends to depict the group as a monolith of which the speaker is not a part. Second, I sharpen the insights of previous research on the social meanings of demonstratives (e.g., Lakoff 1974), and explain why they serve as a useful resource for expressing exclamativity and evaluativity and for promoting a sense of shared perspective and experience between interlocutors. In both cases, I explain the relevant social meanings via the socio-pragmatic framework developed herein: we can understand the social meanings associated with these expressions by examining the expressions' content in the light of contextual (especially social) factors and the content of functionally related alternative expressions. In addition to providing new insights into the social character of English determiners, this work makes the case that social meaning is an indispensable facet of interpretation and use, demonstrates the advantages of pursuing semantic, pragmatic, and sociolinguistic research in tandem, and pushes toward a unifying theory of meaning beyond the literal.
Precise estimation of mutation spectrum from deep sequencing genomic data [electronic resource]
Keywords: Genetics.

The mutation spectrum (MS) is the description of novel mutations. It represents the potential diversity of genetic possibilities available to an evolving population, and is the basis upon which selection acts. A precise understanding of the MS of any species is imperative for accurate modeling of the rate and dynamics of population evolution. However, spontaneous mutations are rare, the characterization of MS has been tedious and difficult, and a precise description of a genome-wide MS from a large number of mutational events wasn't available. In my thesis work, I take advantage of the advent of high-throughput sequencing, and approach the question in three ways. I show that pooled sequencing accurately reflects the SFS of a population with error well approximated by binomial sampling, and is a reliable means of sampling many populations cheaply. I directly survey the yeast MS through large-scale whole genome sequencing of 145 mutation accumulation lines and identify nearly 1000 spontaneous mutation events, the largest at the time. With whole genome sequences from a further 141 strains of yeast, I show that we can identify young polymorphic sites that show no signals of selection and display a mutation spectrum similar to that found in MA experiments. This technique can potentially be modified and applied to any deep population sequencing data, allowing the study of the MS of organisms not previously available through experimental means.
Predicting protein function and protein-ligand interactions through machine learning [electronic resource]
Keywords: Bioengineering.

With modern day advancements in high throughput technology, we have more genomes, sequences, and protein structures available. An important scientific endeavor is to apply this information towards combating human diseases and disorders. Two key steps in this task involve understanding the function of proteins and developing the means to modulate their behavior. Experimental assays do not possess the necessary throughput to characterize in full the function and drug-binding preferences of these many newly identified proteins. Computational assessment is an attractive alternative, but current algorithms possess many shortcomings. Function prediction tools struggle to annotate sequence and structurally unique proteins; ligand-binding predictors have limited accuracy, as they are largely physics-based with many approximations built into the calculation of intermolecular interactions. With the wealth of biological information, specifically protein structure data, there presents an opportunity to take data-driven, machine learning approaches to these scientific questions. This dissertation thus presents novel computational algorithms for predicting protein function and small molecule interactions that merge protein structure data with machine learning. The first method (HMMDF) combines protein sequence models (Hidden Markov Models) with protein structure models augmented by structural dynamics information (Dynamic FEATURE) to identify the function of sequence and structurally novel proteins. HMMDF applied to thioredoxin function prediction shows high precision and recall. The second method (FragFEATURE) addresses the prediction of protein-ligand interactions using an innovative knowledge base of protein structural environments annotated with the small molecule substructures (fragments) they bind. Given a protein structure of interest, FragFEATURE searches the knowledge base for environments similar to the query to identify statistically enriched fragments. FragFEATURE predicts fragments corresponding to known ligands of a protein target with high accuracy; in many cases, FragFEATURE predicts fragments corresponding to known inhibitors of a protein target. Using this fragment binding predictor, I identified fragments for two bacterial proteins involved in pathogenesis and antibiotic resistance. These fragments may lead us to the development of inhibitors for these therapeutically important protein targets. In summary, the work presented in this dissertation represents novel and powerful methods for interrogating protein function and protein-ligand interactions, strengthening the repertoire of computational tools to assist in the understanding and treatment of human diseases and disorders.
Pressure and temperature transient analysis during hydraulic fracturing [electronic resource]
Keywords: Energy Resources Engineering.

Recent developments in bottomhole data acquisition techniques, such as distributed temperature sensing systems (DTS), have brought attention to the potential increase of information that can be obtained from temperature data. Studies have shown the application of temperature surveys to estimate flowrate profiles, resolve the kind of damage around the well, and improve the robustness of the history matching, among others. Nonetheless, Temperature Transient Analysis (TTA) is not a mature technique and its capabilities have not been explored fully yet. In order to investigate the application temperature analysis to the hydraulic fracturing problem, in addition to pressure analysis, a numerical model was developed to calculate pressure and temperature responses. Regarding the fracture and reservoir fluid flow, a general approach can be adopted, where the formation permeability and fracture characteristics dictate how the fluids flow during and after fracture growth. We developed a comprehensive model, which accounts for the pressure effect on the temperature response, as well as a dynamic fracture that grows and eventually is allowed to close during falloff. In this work we analyzed the temperature and pressure responses during and immediately after hydraulic fracturing in order to improve our knowledge of this complicated physical problem. Based on this study, we can better understand not only the fracture properties, but also the reservoir itself. In addition, sensitivity analysis shows how reservoir permeability can impact final fracturing performance, as well as pressure and temperature responses. The developed model was also applied to simulate minifrac analysis, and a field example is presented that shows a good agreement with the simulated behavior during fracture closure. One of the main contributions of this research is related to the creation of multiple fractures along a horizontal well. This type of well and completion technique have become the key factors for success in development of unconventional resources. Both sequential and simultaneous fracture growth was studied. The presence of reservoir permeability heterogeneity was investigated and the capability of temperature data to identify the existence of such reservoir structure was explored. Capabilities and limitations of information carried by temperature data are presented through different geometry analyses. Also related to horizontal multifractured wells, a case was considered in which one of the fractures interconnects different zones vertically. This study is motivated by microseismic evidence of activity captured out of the target reservoir location. The temperature data analysis during the beginning of production life of the well was shown to be very effective to identify the existence of such interconnection. The difference in temperature due to geothermal gradient allows a very clear temperature signature, where the pressure analysis would not reveal the connection.
Pricing Dynamics and Risk Taking in Partially Segmented Incomplete Markets
Keywords:  Department of Economics
China
emerging markets
empirical asset pricing
equity risk premium
market incompleteness
noise trading
portfolio optimization
and twin shares
Stanford Department of Economics
Thesis

I examine the dynamic pricing of multi-listed equity shares from the universe of Chinese companies traded in mainland China (A-shares) and Hong Kong (H-shares). Even though each equity pair represents the identical claims to the residual earnings of a single entity, which the investors are entitled to same dividends and voting rights from either share class, persistent and time-varying differences are priced in the majority of such shares.
In this paper, I provide a review of the extant literature, and subsequently I describe and analyze how different local market environments affect the cross-market A-H share price disparity. Differing from the theory proposed in Heaton and Lucas (1994), I argue the contrary and empirically test whether market incompleteness, notably evidenced by the lack of financial innovations and the capital account restrictions in mainland China, lowers the equity risk premia in the mainland market on an aggregate level. Drawing from the conjecture in Fernald and Rogers (2002), I show that the variation in the aggregate A-H share premia can be explained by the risk profiles of alternative investment vehicles in mainland China. Finally, using a unique account level brokerage data-set, I extrapolate the contributions of noise-driven investors to idiosyncratic mis-pricing through event-driven studies.
Probability models on large random graphs and matrices [electronic resource]
Keywords: Statistics.

This dissertation studies the asymptotic properties of several probability models on graphs and matrices. In the first part, we consider the well known Ising model on large locally tree-like graphs, and show that in the absence of the external magnetic field, in the low temperature regime, the Ising measure decomposes into plus and minus Ising measure. Additionally, we prove a limit result for the Ising measures, conditioned on the sum of the spins being positive, which provides a deeper insight on the behavior of the Ising measures in the limit. In case the graphs are not locally tree-like, we obtain the mean field behavior in high temperature regime, under a certain criterion. In the second part of the thesis, we study limiting spectral distribution of random matrices. Motivated by a conjecture on the limit law of the empirical spectral distribution of uniformly chosen, oriented d-regular graphs, we consider the sum of d i.i.d Haar distributed unitary/orthogonal matrices, and show that its empirical spectral distribution has rotationally invariant limit law on the complex plane, with a explicit density.
Productivity and performance with embedded domain-specific languages [electronic resource]
Keywords: Electrical Engineering.

Modern computing is transitioning from a predominantly sequential processing model to heterogeneous platforms that combine sequential, parallel, specialized, and distributed processors. However, mainstream tools and programming languages have not kept up with these changes. In particular, the generality of high-level programming language abstractions makes it difficult to perform sophisticated parallel optimization or generate code efficiently for different devices. Instead, programmers who require high performance must often abandon high-level abstractions and program using low-level models (e.g. CUDA, OpenCL) or explicitly parallel models and languages (e.g. MapReduce, X10). Implementing a high performance solution is time-consuming, error-prone, and usually not portable to different devices or datasets. This additional programming complexity means that high performance solutions are out of reach for most non-experts. We believe that the specialization of programming languages into domain-specific languages (DSLs) enables new opportunities for productive high performance programming on modern systems. We demonstrate that DSL compilers can exploit high-level data and control structure and domain-specific optimizations to efficiently compile implicitly parallel DSLs to multicore CPUs, GPUs and clusters. By sacrificing generality, we achieve both productivity (DSL users use high-level abstractions to write their program once) and performance (the programs are compiled to run on heterogeneous systems). We use the Lightweight Modular Staging (LMS) and Delite frameworks as a starting point. LMS and Delite are Scala frameworks that provide common, reusable components that simplify the implementation of embedded DSL compilers. We develop OptiML, an implicitly parallel DSL for machine learning, and describe its design and implementation. We show that OptiML is both productive and performant (it outperforms MATLAB and is competitive with hand-optimized C++ in nearly all cases). We present new techniques for composing compiled embedded DSLs, and validate these techniques by implementing and composing new compiled embedded DSLs for data querying (OptiQL), graph analysis (OptiGraph), scientific computing (OptiMesh) and collections (OptiCollections). The ability to compose compiled DSLs makes it possible to use them like libraries while still achieving high performance. Furthermore, we present a case study on the degree of reuse achieved across the DSL implementations and show that common compiler infrastructure has a real-world impact in reducing the effort required to build a DSL. Finally, we introduce Forge, a new meta DSL for DSL development that provides a high-level specification language for parallel and heterogeneous DSLs. This body of work demonstrates for the first time that embedded DSLs can approximate both the productivity of libraries and the performance of hand-optimized code.