VictorJuiz/thesis huggingface dataset:
Two models on limit order trading [electronic resource]

In this thesis we study two limit order trading models based on the one of Avellaneda and Stoikov (2008). For the first one, we study Avellaneda-Stoikov model under the condition when the underlying price is mean reverting. Our main result is that when time is far from the terminal, the optimal price for bid and ask limit orders is constant, which means that it does not track the underlying price. Numerical simulations confirm this behavior. When the underlying price is mean reverting, then for times sufficiently far from terminal, it is more advantageous to focus on the mean price and ignore fluctuations around it. Mean reversion suggests that limit orders will be executed with some regularity, and this is why they are optimal. We also explore intermediate time regimes where limit order prices are influenced by the inventory of outstanding orders. The duration of this intermediate regime depends on the liquidity of the market as measured by specific parameters in the model. The second model we study is a price impact model where the underlying price is affected by the mean field terms representing existing limit orders in the market. The main result on this model is that there are two different regimes for the representative trader in the solution of MFG: In the beginning, he trades aggressively which results in a large price impact; after a short period of time, he trades, by expectation, approximately symmetric about the underlying price. As a function of time, the price impact decays exponentially in the solution of MFG. Moreover the expectations of optimal prices for the representative trader are constants symmetric about a level close to the terminal value of expected underlying price.

Keywords: Mathematics.
Advisor: Papanicolaou George primary advisor. Thesis advisor
Ryzhik Leonid advisor. Thesis advisor
Ying Lexing advisor. Thesis advisor
Two-dimensional spatial imaging of charge transport in germanium crystals at cryogenic temperatures [electronic resource]

In this dissertation, I describe a novel apparatus for studying the transport of charge in semiconductors at cryogenic temperatures. The motivation to conduct this experiment originated from an asymmetry observed between the behavior of electrons and holes in the germanium detector crystals used by the Cryogenic Dark Matter Search (CDMS). This asymmetry is a consequence of the anisotropic propagation of electrons in germanium at cryogenic temperatures. To better model our detectors, we incorporated this effect into our Monte Carlo simulations of charge transport. The purpose of the experiment described in this dissertation is to test those models in detail. Our measurements have allowed us to discover a shortcoming in our most recent Monte Carlo simulations of electrons in germanium. This discovery would not have been possible without the measurement of the full, two-dimensional charge distribution, which our experimental apparatus has allowed for the first time at cryogenic temperatures.

Keywords: Physics.
Advisor: Cabrera Blas primary advisor. Thesis advisor
Graham Peter (Peter Wickelgren) advisor. Thesis advisor
Irwin Kent advisor. Thesis advisor
Two-way coupled compressible euler-lagrangian simulation to investigate cavitation and bubbly shock propagation [electronic resource]

Cavitation is one of the most significant performance limiting phenomena encountered in liquid handling machinery and marine propulsion systems. Control of cavitation plays a critical role in the design of such systems. Ineffective control of cavitation can result in a significant erosive damage to marine propellers, decreased efficiency, unexpected noise/vibration and limited thrust of propulsion systems. This study aims at physics-based modeling of inter-phase phenomena encountered in cavitating flows to develop a compressible Eulerian-Lagrangian (E-L) framework for cavitating and non-cavitating bubbly flows. A fine-resolution DNS was performed to investigate wall reflection of bubbly pressure wave and to model bubble-bubble interaction from Rayleigh-Plesset equation. The amplification of pressure at wall is a function of bubble size, magnitude of incident pressure wave and surface tension. It does not depend on void fraction, which implies that even the presence of small bubbles near a wall in two-phase flows with low void fraction can cause damage to wall surface. A two-way coupled compressible E-L framework has been developed. Such methodology for predicting micro-dynamics of bubbles in bubbly shock problem worked very well to match FT-DNS result, which is a fine resolution DNS that tracks/resolves interface between two phases. Even with small number of bubbles in a 2-D geometry fundamental cavitation phenomena such as formation/detachment of sheet cavity due to re-entrant jet and development into cloud cavity could be observed. Bubble-bubble interaction is crucial for compressible E-L framework in terms of stability and accuracy, particularly for cavitating flows with high number density of bubbles. A bubble-bubble interaction model is specially designed for kernel interpolation based E-L solvers and Kubota's BBI model.

Keywords: Mechanical Engineering.
Advisor: Lele Sanjiva K. (Sanjiva Keshava)
Ultrafast electrically driven processes in phase change materials [electronic resource]

Phase change materials are being actively developed for nonvolatile memory applications due to their ability to switch between crystalline and amorphous phases, with highly contrasting electrical and optical properties, controllable by application of electrical pulses. In particular, the crystallization occurs through a thermal mechanism when the amorphous phase is heated beyond the crystallization temperature. In order to achieve these temperatures using voltages found in ordinary electronic devices, it is important that the highly resistive amorphous phase undergoes a so-called "threshold switching" process, whereby a sufficiently strong applied field induces a drastic increase in material conductivity. This process has been studied for decades but is still not well understood, with recent experiments suggesting a possible lower limit for the onset time of threshold switching, of order a few nanoseconds. Here, single cycle picosecond-duration terahertz pulses, generated from a pulsed laser source, are used as an ultrashort electric field bias, in order to investigate the picosecond time-scale response of amorphous phase change materials Ge2Sb2Te5 and Ag4In3Sb67Te26. Our results indicate that threshold switching is able to occur within this sub-picosecond pulse duration. Upon application of a series of pulses above a threshold of a few hundred kV/cm, we observe terahertz-driven filamentary crystallization, as evidenced by optical microscopy, microwave impedance microscopy, and x-ray diffraction studies. These results suggest that threshold switching occurs through a field-driven electronic tunneling process. Therefore, threshold switching is not a time-limiting factor for the thermal crystallization process, and amorphous phase-change materials could be considered for applications as ultra-fast resistance switches.

Keywords: Applied Physics.
Advisor: Lindenberg Aaron Michael primary advisor. Thesis advisor
Reis David A.
Reed Evan J. advisor. Thesis advisor
Ultrafast terahertz-driven ionic response in thin film ferroelectric oxides [electronic resource]

Ferroelectric materials comprise non-centrosymmetric unit cells with permanent electric dipole moments switchable by electric fields, exhibiting strong coupling between polarization, strain, and electronic degrees of freedom. The dynamics of the ferroelectric polarization underlies their functionality but these processes, and the speed limits determining how fast the polarization can change, remain largely unknown. In particular, the ability to all-optically generate significant modulations or reorientations in the ferroelectric polarization represents a key step towards terahertz-frequency information storage technologies, actuators, and optical modulators. From a more general perspective, the use of light to modulate the functional properties of ferroelectrics holds promise for both directing these degrees of freedom and for elucidating their fundamental properties. Previous theoretical predictions indicate that large-amplitude polarization modulations can be achieved on hundreds of femtosecond time-scales, and studies with hundreds of picosecond to nanosecond time-resolution using electrical bias pulses have provided evidence for large amplitude modulations or switching of the polarization. Here we use terahertz pulses as an all-optical bias to apply sub-picosecond duration electric fields to BiFeO3 and BaTiO3 thin films. We show that these generate large amplitude changes in the ferroelectric polarization. In BiFeO3 we used the second harmonic light generated by the thin film as a structural probe and observed modulations in the intensity of second harmonic light corresponding to on-off ratios of 220x, gateable on few hundred femtosecond time-scales. These effects are enhanced through the use of rare-earth-doping to position the sample at a morphotropic phase boundary where the electromechanical and nonlinear-optical responses are enhanced but where the dynamical response of these materials has not previously been explored. In BaTiO3, we probed the atomic-scale response by femtosecond x-ray scattering techniques. We show that electric fields applied perpendicular to the ferroelectric polarization drive large amplitude displacements of the titanium atoms along the ferroelectric polarization axis, comparable to that of the built-in displacements associated with the intrinsic polarization. These transient polarization modulations are followed by long-lived vibrational heating effects driven by resonant excitation of the ferroelectric soft mode, as reflected in changes in the c-axis tetragonality. These measurements define the fastest speeds on which nanoscale ferroelectric devices can be manipulated by electric fields and open up novel opportunities for dynamic optomechanical strain engineering without activation of electronic degrees of freedom.

Keywords: Electrical Engineering.
Advisor: Lindenberg Aaron Michael primary advisor. Thesis advisor
Fan Shanhui
Wang Shan X. advisor. Thesis advisor
Uncovering patterns in student work [electronic resource] : machine learning to understand human learning

When millions of students learn by working through the same curriculum what patterns unfold? Being able to autonomously understand students learning, both in terms of assessing knowledge and being able to provide feedback, is a grand challenge in education and one that has been studied for many years. Massive online classes, which were recently popularized, have provided a serendipitous opportunity to break ground on this important problem. In my dissertation I will explore data driven approaches in the domain of students learning to program -- a discipline with rich, structured assignments. I will discuss emergent patterns in; the space of student partial solutions, in how students navigate open-ended assignments and in how students work through a series of problems. Highlights include (1) the first application of deep learning to student trajectories which yields a noteworthy improvement in the state of the art for the task of Knowledge Tracing (2) discovery of a pattern in how students navigate solution spaces that both predicts how teachers would suggest a learner make forward progress and has an almost perfect logarithmic relationship with the probability of a student succeeding in the future and (3) A method to apply deep neural networks to autonomously embed student programs into Euclidian space. This work has been in collaboration with some of the largest online platforms to date: Khan Academy, Coursera and Code.org. My thesis is meant to depict a nascent subspace of education research, and where it could go. The results I present more than anything show the extent to which many open, fascinating problems remain.

Advisor: Guibas Leonidas J. primary advisor. Thesis advisor
Mitchell John C. advisor. Thesis advisor
Sahami Mehran advisor. Thesis advisor
Uncovering the neural representation of multiple dimensions of object categorization in human visual cortex [electronic resource]

We rely on vision more than on any other sensory modality to interact with and make sense of the world. Our behavior and culture, as well as the data we generate all rely strongly on visual information to index and capture salient relationships in the world. Within this realm, categorization is a fundamental building block of our visual experience. It is due to this marvelous generalization process that we take the problem of perceiving and understanding trillions of entities in our world (objects and scenes) and reduce it to a more manageable magnitude by binning virtually everything we see into a few tens of thousands of categories. Thus, it becomes a fundamental problem in understanding human vision to elucidate the mechanisms by which our visual cortices extract such complex information from a noisy sea of colored dots encoded by our retinas when we look out into the world. But what represents a 'good' category and why do these distinctions emerge the way they do? Cognitively, useful distinctions between groups of items simultaneously maximize within-category similarity and between-category dissimilarity. The underlying hypothesis behind the work we put forward in this dissertation is that this key idea of similarity maximization also extends to the instantiation of neural patterns of representation in visual cortex. To this end, we use computational approaches in the context of several functional neuroimaging (fMRI) experiments to explore how behaviorally pervasive dimensions of object categorization, such as hierarchical organization and typicality, are represented in the brain and how they help us build a coherent picture of the world. Finally, we propose and test a model of neural object category processing based on the hypothesis that the cognitive utility of category structure partly drives information processing in visual cortex.

Advisor: Li Fei Fei
Chamberlain Narcisse
Ermon Stefano advisor. Thesis advisor
Grill-Spector Kalanit advisor. Thesis advisor
Understanding degradation mechanisms and improving long-term performance of organic solar cells [electronic resource]

Organic photovoltaics (OPVs) attract interest because they can be printed rapidly and at low cost. Over the past decade, research directed towards increasing the initial power conversion efficiency has pushed the record device efficiency above 10%. However, in order to be a competitive technology, the performance must remain relatively stable over time. In this talk, I discuss mechanisms of degradation in materials and solar cells as well as suggest materials properties that will improve stability. First I discuss the bleaching of the absorber materials in air. I will show that dense, ordered film morphologies can stabilize materials -- in one case, by a factor of 2000. Next, I show that temperature induced degradation can occur in well-encapsulated solar cells when the materials have glass transition temperatures at or below operating conditions. Similarly, I discuss the effect of mobile molecules present in the films and show that purification aimed at reducing their content can improve thermal stability. In a solar cell made from a polymer with a high glass transition temperature, lifetimes can exceed 20 years. Finally, I discuss the light-induced degradation that occurs in the first several hundred hours of solar cell operation, even in the absence of oxygen. This degradation can reduce the short circuit current via a fullerene dimerization reaction, or it can reduce the open circuit voltage via the introduction of energetic disorder on the semiconducting polymer. I show that both degradation modes are reduced in films with a more ordered morphology. These findings provide guidelines for reducing thermal and photo-induced degradation and improving the long-term stability of OPVs.

Keywords: Materials Science and Engineering.
Advisor: McGehee Michael
Bao Zhenan advisor. Thesis advisor
Salleo Alberto
Understanding material limits for encapsulated combo sensor design and fabrication [electronic resource]

Due to growing demand in the consumer space, there has been a push to create combo sensors and to reduce overall chip size. The 'epi-seal' process, developed by Robert Bosch GmBH and Stanford University, enables wafer-scale packaging of devices in an environment that has shown no long-term aging effects, using a thin-film encapsulation that results in the smallest reported package size. In order for this process to become truly viable for co-fabrication of sensors with different design constraints, we made several process modifications to produce sensors with high-sensitivity but low cross-sensitivity between different environmental factors. To prove this concept, we co-fabricate X/Y and Z-axis accelerometers, a pressure sensor, and resonant thermometer, where cross-sensitivity is improved by more than 700x; remaining cross-sensitivities can then be compensated based on real-time measurements from other sensors. In order for sensors to scale down and sustain sensitivity performance, I examine two material limits that can be prohibitive to sensor miniaturization: stiction and fatigue. Stiction studies with as-fabricated epi-seal devices reveal adhesion forces between contacting surfaces that are asperity-dominated and on average 35 μN. In order to overcome this force, I investigate several dynamic anti-stiction methods that lead to effective stiction forces being reduced by over 60%. Fatigue studies using high-displacement resonators fabricated in the epi-seal process were performed to observe any potential aging effects in single-crystal silicon. In contrast to literature, long-term (> 10^10 cycles) fatigue experiments with these devices showed no signs of fatigue damage up to 7.5 GPa. A cross-comparison with these literature results reveals that fatigue can indeed be prevented, only with atomically smooth side-walled devices that are fabricated in a packaging technology that prevents stress corrosion.

Keywords: Mechanical Engineering.
Advisor: Kenny Thomas William primary advisor. Thesis advisor
Howe Roger Thomas advisor. Thesis advisor
Nelson Drew advisor. Thesis advisor
Understanding the Language of Genomes through Taxonomic Classification of Protein Domain Architectures

The protein universe, or the set of all proteins of all biological organisms on earth, is vast, rapidly growing, and largely uncharacterized. To better study the millions of proteins that have been sequenced, we utilize domain architectures, which annotate proteins with sequence profiles that correlate with structural domains. If proteins are considered to be the language of a genome, we can think of sequence profiles as “words” and domain architectures with more than one sequence profile as “sentences” (Scaiewicz & Levitt, 2015). This linguistic analogy allows us to use text classifiers to classify proteins into taxonomic groups or kingdoms. We built three text classifiers: (1) dependent only on word content of a domain architecture and based off Naïve Bayes, (2) dependent only on word order in a domain architecture and based off Markov chains, and (3) a combination of the first two. We found that Archaea and Viruses performed better with the order-independent classifier (1), indicating that their vocabulary is the defining feature of their language. Eukaryotes and Prokaryotes performed better using the combination method (3), showing that word order (a very simple grammar) also plays a role in their languages. We found significant relationships between fraction of unique words and sentences in a language to the ability to predict that language. The relationship was linear between unique sentences and prediction ability but logarithmic between unique words and prediction ability. Kingdoms of Life (Eukaryotes, Prokaryotes, Viruses and Archaea) occupied the horizontal portion of the logarithmic curve that approached perfect prediction, but plants also were on that part of the curve (between Archaea and Viruses), establishing the vocabulary of plants as almost as distinct as that of a kingdom. Language analysis on proteins has assisted in identifying trends in a vast, poorly understood, but very important area of science.

Keywords:  Department of Biology
Biology
Domain Architecture
Protein Sequence
Protein Structure
Structural Genomics
Thesis
Advisor: Levitt Michael
Petrov Dmitri
Understanding the pathogenesis of age-related knee osteoarthritis [electronic resource]

Osteoarthritis (OA) is clinically characterized by the degeneration of articular cartilage and affects 27 million people in the United States alone. OA is an irreversible disease that can be particularly debilitating in load-bearing joints such as the knee, and costs the U.S. over

Keywords: Bioengineering.
Advisor: Andriacchi Th. P. (Thomas P.) primary advisor. Thesis advisor
Delp Scott advisor. Thesis advisor
Gold Garry E.
Understanding the role of histone H3 lysine 36 methylation in chromatin regulation and cancer [electronic resource]

Methylation of histone H3 at lysine 36 (H3K36) is found at gene bodies of actively transcribed genes, but the state of methylation at this residue defines distinct biological outcomes. Trimethylation of this site (H3K36me3), which is mediated by the KMT SETD2 in humans, is involved in regulation of RNA processing. Loss of SETD2 and H3K36me3 is a recurring phenomenon in clear cell renal cell carcinoma (ccRCC) and other cancers, suggesting a tumor suppressor role for SETD2. In contrast, the specific molecular functions associated with H3K36me2 are unclear. However, elevated levels of this modification lead to aberrant activation of normally silenced genes, and upregulation of this mark is linked with numerous cancer types including acute myeloid leukemia, multiple myeloma, lung cancers, breast cancers, and glioblastomas. Given their clear implications for opposing roles in disease, it is important to understand the molecular functions of H3K36me2 and H3K36me3 in both their normal physiological roles and disease states. In my thesis, I first compare the roles of H3K36me2 and H3K36me3 in the regulation of actively transcribed genes. Using analyses of RNA-seq datasets from cells depleted of each modification, I show H3K36me3, but not H3K36me2, plays an important role in regulating gene expression and splicing processes. Then, I characterize the effects of H3.3 K36M, an oncohistone, on global regulation of H3K36 methylation. I demonstrate that this mutation depletes not only H3K36me3 (as previously reported) but also H3K36me2, and that the K36M mutant mimics loss of H3K36me2 alone in proliferation assays. Assaying H3K27me3 levels under H3K36 depletion indicates that the crosstalk between H3K27 and K3K36 methylation is largely mediated by H3K36me2, not me3 as previously assumed. This work points to an important role of H3K36me2, not just H3K36me3, in the dysregulation of chromatin modifications mediated by the H3.3 K36M oncohistone. Lastly, I identify the first reader for the H3K36me2 modification. I show that a PWWP domain on the NSD2 protein preferentially binds to H3K36me2. This PWWP-H3K36me2 interaction seems to be important for the stability of NSD2 at chromatin, as well as its ability to increase global H3K36me2 levels in cells and promote cell proliferation. Based on these data, I propose a model for H3K36me2 propagation by NSD2 sensing its own catalytic product and spreading it through chromatin regions.

Keywords: Biology.
Advisor: Gozani Or Pinchas primary advisor. Thesis advisor
Chua Katrin Faye advisor. Thesis advisor
Morrison Ashby J.
Sage Julien advisor. Thesis advisor