VictorJuiz/thesis huggingface dataset:
The poetics of dehumanization in modern Jewish literature [electronic resource]

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.

Keywords: Comparative Literature.
Advisor: Greene Roland
Eshel Amir advisor. Thesis advisor
Gumbrecht Hans Ulrich advisor. Thesis advisor
Safran Gabriella
Shemtov Vered Karti advisor. Thesis advisor
A positron emission tomography (PET) neuro insert for combined PET/magnetic resonance imaging [electronic resource]

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.

Keywords: Bioengineering.
Advisor: Levin Craig primary advisor. Thesis advisor
Glover Gary H.
Pratx Guillem advisor. Thesis advisor
Positron emission tomography (PET)-guided dynamic tumor tracking for radiation therapy [electronic resource]

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.

Keywords: Electrical Engineering.
Advisor: Graves Edward (Edward Elliot)
Keall Paul advisor. Thesis advisor
Levin Craig advisor. Thesis advisor
Pauly John (John M.) advisor. Thesis advisor
The power of the unseparate/network aesthetics and the rise of interdisciplinary art [electronic resource]

It has become common practice in the visual arts to work across disciplines. Rather than devoting oneself exclusively to being a painter, sculptor, photographer or performance artist, a contemporary practitioner is just as likely to deploy whatever disciplines in combination are deemed most beneficial to manifest the work's concept without disciplinary boundaries. The subject of the current study concerns itself with certain origins and models for interdisciplinary artistic production in the modernist period, with references forward to the present. Richard Wagner, Paul Cezanne, Marcel Duchamp, and the pedagogical structure created by Walter Gropius at the Bauhaus each point toward contemporary interdisciplinary practice in unique ways that are analyzed here. These late nineteenth-century and early twentieth-century practices are seen as well within the framework of what is defined in the text as network aesthetics. Network aesthetics itself is described not only in terms of network theory but also in philosophical terms, based on concepts in Hegel and in more recent phenomenological thought, such as the work of Merleau-Ponty. While the main trope used in discussing modernist art describes exemplary works in terms of the fragment and fragmentation, this study concerning the rise of interdisciplinary artistic production argues for the equal importance in modernist and contemporary art of unification and wholeness. Network aesthetics is a way of capturing this idea, and the works examined here are discussed in terms of their network characteristics toward these ambitions of unification and wholeness.

Advisor: Lee Pamela M. primary advisor. Thesis advisor
Schnapp Jeffrey T. (Jeffrey Thompson)
Turner Fred advisor. Thesis advisor
Pragmatics and the social meaning of determiners [electronic resource]

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.

Keywords: Linguistics.
Advisor: Eckert Penelope primary advisor. Thesis advisor
Potts Christopher
Podesva Robert advisor. Thesis advisor
Precise estimation of mutation spectrum from deep sequencing genomic data [electronic resource]

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.

Keywords: Genetics.
Advisor: Petrov Dmitri Alex
Bustamante Carlos (Carlos D.)
Sherlock Gavin
Tang Hua (Geneticist)
Predicting protein function and protein-ligand interactions through machine learning [electronic resource]

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.

Keywords: Bioengineering.
Advisor: Altman Russ primary advisor. Thesis advisor
Huang Kerwyn Casey
Pande Vijay advisor. Thesis advisor
Pricing Dynamics and Risk Taking in Partially Segmented Incomplete Markets

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.

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
Advisor: Shoven John
Private functional encryption and secure deduplication [electronic resource]

We consider the security models of two novel encryption schemes: identity-based encryption (IBE) and its generalization to functional encryption (FE), and a new primitive denoted message-locked encryption (MLE). These concepts are motivated by applications such as routing encrypted email and deduplicating encrypted data. Identity-based encryption systems can be used to build encryption schemes that allow searching on encrypted data. We put forward a new notion of security for IBE and FE, called function privacy, that ensures that secret keys do not reveal unnecessary information about the functionality enabled by the key. We develop a general approach to constructing function-private IBE schemes from a variety of standard cryptographic assumptions and extend our ideas to consider a more expressive class of functions. Such schemes enable searching on encrypted data without revealing what is being searched for. In a similar vein, we show that we can strengthen the security notions proposed originally for message-locked encryption (MLE) to consider a larger and more realistic class of adversaries. We also propose constructions for secure deduplication schemes that satisfy these security notions.

Advisor: Boneh Dan
Trevisan Luca advisor. Thesis advisor
Williams Ryan (Richard Ryan) advisor. Thesis advisor
Probability models on large random graphs and matrices [electronic resource]

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.

Keywords: Statistics.
Advisor: Dembo Amir primary advisor. Thesis advisor
Chatterjee Sourav advisor. Thesis advisor
Montanari Andrea advisor. Thesis advisor
Productivity and performance with embedded domain-specific languages [electronic resource]

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.

Keywords: Electrical Engineering.
Advisor: Olukotun Oyekunle Ayinde primary advisor. Thesis advisor
Kozyrakis Christoforos
Ré Christopher advisor. Thesis advisor
Provenance, paleogeography, and mass-movement of deep-water depositional systems in arc-adjacent basins [electronic resource] : the cretaceous-paleogene California forearc and upper miocene mohakatino formation, New Zealand

Plate convergence and associated subduction result in a dynamic setting where the development and evolution of sedimentary systems are influenced by a variety of autogenic and allogenic processes. Correspondingly, patterns of sedimentation within basins that form trench-ward of magmatic arcs (i.e., forearc basins) are often complex as a result of the interplay between interrelated factors that include: (1) magmatism and orogenesis in the hinterland, (2) changes in subduction regime (e.g., steep versus shallow angle of plate descent), and (3) local basin deformation. This dissertation examines the tectonic and sedimentary processes that acted in concert to influence the evolution of two arc-adjacent sedimentary systems: the Late Cretaceous-Eocene California forearc (Chapters 1 and 2) and the late Miocene Taranaki Basin, New Zealand (Chapter 3). These three chapters include investigation of a variety of processes that occur at different temporal and spatial scales. Specifically, major themes of this dissertation include (1) the evolution of sediment dispersal patterns in response to changes in continental arc magmatism and subduction regime (Chapter 1), (2) the development and early history of the central San Andreas fault system and the paleogeographic setting of central California (Chapter 2), and (3) submarine mass-movement and patterns of soft-sediment deformation within a basin-floor sedimentary succession (Chapter 3). The data types and methods used in each chapter are diverse and include detrital zircon U-Pb geochronology, subsurface datasets (e.g., well logs and seismic-reflection data), outcrop description, and structural analysis.

Keywords: Geological and Environmental Sciences.
Advisor: Graham S. A. (Stephan Alan)
Grove Marty
Lowe Donald R.