VictorJuiz/thesis huggingface dataset:
Developing a Curriculum for a Formal Education Setting to Prevent Child Marriage in Rural Areas of Honduras: A Design-Based Research Study

Latin America and the Caribbean is the only region in the world where child marriage (CM) is not decreasing. Growing levels of education and legislation to ban this practice have not been associated with CM reduction. This dissertation is grounded on the belief that the educational sector can contribute to CM prevention by going beyond expanding access to secondary education. Schools can also contribute to CM prevention by focusing on challenging social norms, understanding the role of the different changes that occur during adolescence, and recognizing the structural limitations that drive CM. This design-based research study provides a deeper understanding of the factors that shape adolescents’ decisions to marry in rural areas of Honduras and uses this knowledge to develop a theory of action to design, implement, and evaluate a school-based curriculum to prevent CM. The results and experience of this investigation advanced the development of design principles that can guide the implementation of interventions in similar contexts throughout Latin America./nKeywords: child marriage, Latin America, design-based research

Advisor: Murphy-Graham, rin
Flocks of Artificially Intelligent Swimming micro-Robots with Long-range Hydrodynamic Interaction and Objectives

This dissertation addresses various aspects of realizing a three-dimensional (3D) controlled flock of swimming micro-robots that operate in, and cooperatively influence, viscous fluid environments. A systematic approach is then presented to equip the agents with an adaptive decision-making intelligence, so as to enable flocks of these artificially intelligent swimming micro-robots to achieve various objectives in the presence of flow-mediated interactions./nIn the first part of this dissertation, we introduce a versatile swimming robot with full 3D maneuverability in viscous environments. The experimental realization of this artificial low-Reynolds swimmer is then reported, and a hierarchical control strategy is implemented to perform various swimming maneuvers. The major challenge, which makes the swarm-control of swimming micro-robots substantially different from other well-studied swarms, is the presence of long-range flow-mediated (i.e. hydrodynamic) interactions. Therefore, the second part of this dissertation is devoted to the investigation of swarm hydrodynamics, including mutual interactions between these micro-swimmers, and their behavior in vicinity of solid boundaries. In particular, we unveil orbital topologies of interacting micro-swimmers, and report diverse families of attractors including dynamical equilibria, bound orbits, braids, and pursuit-evasion games. The third part of this dissertation is focused on optimal swarm-control strategies for swimming micro-robots to achieve various objectives in the presence of flow-mediated interactions. We show that micro-swimmers can form a concealed swarm through synergistic cooperation in suppressing one another's disturbing flows. Various control schemes are then demonstrated for the concealed swarming and stealthy maneuvers of swimming micro-robots. We also discuss how state-of-the-art reinforcement learning algorithms can be used to realize flocks of artificially intelligent swimming micro-robots. Specifically, a systematic approach is presented to equip the swimming micro-robots with an adaptive decision-making intelligence in response to non-linearly varying hydrodynamic loads. Flocks of these artificially intelligent micro-swimmers are then deployed to actively cloak swimming targets in a crowded environment. This study provides a road-map toward engineering cooperative flocks of smart micro-swimmers capable of accomplishing a new class of group-objectives. We, therefore, hope that it will spur further research on this field at the intersection of fluid mechanics, robotics and artificial intelligence.

Advisor: Alam, Mohammad-Reza
On the Biosynthesis of Triacsins

Natural products are a source of engineering innovation and design for small molecules due to their relevance to wide swaths of the chemical sector including medical, agricultural, food and fragrance, and commodity chemical fields. Their structural complexity comprising of numerous chiral centers and an abundance of heteroatoms makes organic synthesis challenging, expensive, and generally infeasible. While combinatorial chemistry hoped to address these obstacles by enabling rapid diversification and screening methods, it is still limited by access to an initial scaffold on which to act upon. As such there is great potential for leveraging biosynthesis in combination with synthetic strategies to facilitate sustainable production of new bioactive compounds. In this manuscript we present our findings on the biosynthesis of NN bonds in the context of the triacsin natural product family. By elucidating the biosynthesis of a compound with multiple NN chemical bonds, we have discovered multiple enzymatic strategies employed by nature to create a linkage which is synthetically challenging due to the inherent nucleophilicity of nitrogen atoms. As such this research provides insight into the biogenesis of NN bonds and addresses the aforesaid synthetic challenges in chemical access to new bioactive compounds./nTriacsins are notable for the conserved N-hydroxytriazene moiety that all members of the family bear. In addition to two sequential NN bonds, the terminal nitrogen itself is a member of an additional heteroatom-heteroatom linkage in the form of a nitrogen-oxygen bond. As with many stories in natural product biosynthesis, this manuscript begins with the genomic sequencing of the originally reported native producer of triacsins. Mutagenesis and isotopically labeled precursor feeding led to the identification of the essential genes required for triacsin biosynthesis and led to the discovery of another native triacsin-producing organism. Cultivation of mutant strains and analysis of organic extracts from said strains led to the structural characterization of a key late-stage chemical intermediate that informed the biochemical timing of N-hydroxytriazene biosynthesis. Subsequent in vitro reconstitution of several encoded enzymes has advanced our knowledge of biochemical strategies for NN bond biogenesis. Finally, nascent work on the detailed characterization of an NN bond-forming enzyme will provide a full mechanistic understanding of this catalytic transformation. Collectively, this work contributes to the biocatalytic formation of NN bonds.

Advisor: Zhang, Wenjun
Mechanical Behavior of Materials at Multiscale Peridynamic Theory and Learning-based Approaches

Classical continuum mechanics has been widely used in the failure analysis of materials for decades. However, spatial partial derivatives in governing equations of the conventional theory are not valid along the discontinuities. Alternatively, peridynamic (PD) theory, as a nonlocal continuum theory, eliminates this shortcoming by using integro-differential equations which do not contain spatial derivatives. This feature makes PD theory very attractive for problems including discontinuities such as cracks./nIn this study we have extended PD formulation to multiscale problems involving friction, wear, and delamination of thin films. We have formulated a nonlocal ordinary state-based peridynamic formulation for plastic deformation based on the idea of mechanical sublayers which is successfully applied in modeling ductile fracture. In addition, we demonstrated how PD can be used as an efficient and accurate analysis tool in designing real-world applications such as body armor systems using bio-inspired structures with the goal of minimizing the effect of the ballistic impact and bullet penetration depth while being lightweight and comfortable to wear. All our obtained results are validated against experimental observations and an excellent agreement has been achieved. Similar to other mesh-free methods, PD is massively parallelizable. We built parallel PD algorithms leveraging shared and distributed memory systems on CPU as well as CUDA architecture on GPU. We provide extensive experiments showing scalability and bottlenecks associated with each parallelization technique./nIn the second part of this dissertation, we introduce a new class of learnable forward and inference models, using graph neural networks (GNN) which develops relational behavior between material points. We demonstrate these models are surprisingly accurate to generalize remarkably well to challenging unseen loading conditions. Our framework offers new opportunities for harnessing and exploiting non-local continuum theory and powerful statistical learning frameworks to take a key step toward building accurate, robust, and efficient patterns of reasoning about materials behavior.

Advisor: Steigmann, David
Darrell, Trevor
Little String Defects and Nilpotent Orbits

In this thesis, we first derive and analyze the Gukov-Witten surface defects of four-dimensional N = 4 Super Yang-Mills (SYM) theory from little string theory. The little string theory arises from type IIB string theory compactified on an ADE singularity. Defects are introduced as D-branes wrapping the 2-cycles of the singularity. In a suitable limit, these become defects of the six-dimensional superconformal N = (2, 0) field theory, which reduces to SYM after further compactification./nWe then use this geometric setting to connect to the complete nilpotent orbit classification of codimension-two defects, and find relations to ADE-type Toda CFT. We highlight the differences between the defect classification in the little string theory and its (2, 0) CFT limit, and find physical insights into nilpotent orbits and their classification by Bala-Carter labels and weighted Dynkin diagrams.

Advisor: Aganagic, Mina
Stochastic Models for the Control of Mosquito-borne Pathogens

In this dissertation mechanistic stochastic models of mosquito population dynamics rel- evant for the control of mosquito borne pathogens are discussed. The first chapter re- examines the classical theory of vector control, which has been developed since its in- ception over a century ago and has produced a set of quantitative metrics that are the basis for measuring pathogen transmission. One metric is vectorial capacity, which de- scribes the ability of a local mosquito population to transmit pathogens, expressed in a single equation. Despite its appealing simplicity, the formula is too coarse a description to describe mosquitoes in any particular place. Vectorial capacity is reevaluated as an emergent property arising from how mosquitoes use resources on a landscape accord- ing to biological imperatives, using a stochastic model based on behavioral state transi- tions. The second chapter builds a simulation modeling framework to evaluate the effects of gene drive and other genetic control strategies on epidemiological and entomological outcomes. The simulation modeling framework is constructed using stochastic Petri nets (SPN), a mathematical modeling language that succinctly expresses state and events in a bipartite network. The SPN can be interpreted as describing either a deterministic or stochastic system, and associated software is developed to numerically solve the resulting system of ordinary differential equations or continuous-time Markov chain. In the final chapter, an algorithm is developed to simulate stochastic jump processes as disaggregated agent-based models (ABM). To speed up simulation times, the algorithm approximates a subset of hazard rates used to specify the model, but also converges upon the true process as the time step goes to zero. The simulation technique is relevant to a wide variety of contagion processes of interest to epidemiology, and also related fields such as ecology and the quantitative social sciences.

Advisor: Marshall, John M
Line defects in 5d gauge theories

This dissertation has two parts. The first part is devoted to the study of a line(ray) operator in 5d SCFTs with exceptional group global symmetry. We construct an index for BPS operators supported on a ray in five dimensional superconformal field theories with exceptional global symmetries. We compute the $E_n$ representations (for $n=2,\dots,7$) of operators of low spin, thus verifying that while the expression for the index is only SO$(2n-2)\times$U(1) invariant, the index itself exhibits the full $E_n$ symmetry (at least up to the order we expanded). The ray operators we studied in 5d can be viewed as generalizations of operators constructed in a Yang-Mills theory with fundamental matter by attaching an open Wilson line to a quark. For $n\leq 7$, in contrast to local operators, they carry nontrivial charge under the $\mathbb{Z}_{9-n}\subset E_n$ center of the global symmetry. The representations that appear in the ray operator index are therefore different, for $n\le 7$, from those appearing in the previously computed superconformal index. For $3\le n\le 7$, we find that the leading term in the index is a character of a minuscule representation of $E_n$. We also discuss the case $n=8$, which presents a unique technical challenge, and remains an open problem./nThe second part discusses line defects in 5d non-commutative Chern-Simons theory. We studied aspects of a topologically twisted supergravity under Omega background and its interpretation as the bulk side of topological subsector of AdS/CFT correspondence. The field theory side is a protected sub-sector of a specific 3d $\mathcal{N}=4$ gauge theory(from M2-branes), especially its Higgs branch, whose chiral ring deformation-quantizes into an algebra via the $\Omega-$background. The line defect comes from this 3d system. The bulk side is interestingly captured by a field theory again, a 5d Chern-Simons theory, which is topological in 1 dimension and holomorphic in 4 dimensions. The statement of topological holography is an isomorphism between the operator algebras. It is possible to introduce M5-brane to decorate the relation. It acts like a module of the algebra(of M2-brane) in the field theory side and a chiral algebra that interacts with 5d Chern-Simons in the gravity side.

Advisor: Ganor, Ori J
Exploiting Randomness in Computational Cameras and Displays

Despite its desirability, capturing and displaying higher dimensional content is still a novelty since image sensors and display panels are inherently 2D. A popular option is to use scanning mechanisms to sequentially capture 3D data or display content at a variety of depths. This approach is akin to directly measuring (or displaying) the content of interest, which has low computational cost but sacrifices temporal resolution and requires complex physical hardware with moving parts. The exacting specifications on the hardware make it challenging to miniaturize these optical systems for demanding applications such as neural imaging in animals or head-mounted augmented reality displays./nIn this dissertation, I propose moving the burden of 3D capture from hardware into computation by replacing the physical scanning mechanisms with a simple static diffuser (a transparent optical element with pseudorandom thickness) and formulating image recovery as an optimization problem. First, I highlight the versatility of the diffuser by showing that it can replace a lens to create an easy-to-assemble, compact camera that is robust to missing pixels; although the raw data is not intelligible by a human, it contains information that we extract with optimization using an efficient physically-based model of the optics. Next, I show that the randomness of the diffuser makes the system well-suited for compressed sensing; we leverage this to recover 3D volumes from a single acquisition of raw data. Additionally, I extend our lensless 3D imaging system to fluorescence microscopy and introduce a new diffuser design with improved noise performance. Finally, I show how incorporating the diffuser in a 3D holographic display expands the field-of-view, and I demonstrate state-of-the-art performance by using perceptually inspired loss functions when optimizing the display panel pattern. These results show how randomness in the optical system in conjunction with optimization-based algorithms can both improve the physical form factor and expand the capabilities of cameras, microscopes, and displays.

Advisor: Waller, Laura
Ng, Ren
Modeling Visual Minutiae: Gestures, Styles, and Temporal Patterns

The human visual system is highly adept at making use of the rich subtleties of the visual world such as non-verbal communication signals, style, emotion, and the fine-grained details of individuals. Computer vision systems, by contrast, excel in categorical tasks, such as classification and detection, where training often relies on single-word or simple bounding-box annotations. These simple annotations do not capture the richness of the visual world which is often hard to describe in words or localize in an image. Our current systems are thus left to only make use of the obvious, easily describable parts of the visual input. This dissertation investigates several initial directions toward modeling visual minutiae and endowing computer vision systems with rich perception./nPart I describes methods for learning directly from video data without the need for human-provided annotations. The section begins by discussing the use of multi-modal correlations between audio and motion for modeling conversational gestures---an essential part of human communication that is currently ignored by machine perception. The section then proposes a simple method for capturing the appearance details of individual people in motion, which can be used to implement a "do-as-I-do'' motion-transfer application./nPart II explores ways to discover temporal visual patterns in historical data. The section begins by discussing data-mining methods in a dataset of historical high school yearbook portraits where fashion and behavioral styles change over time. The rest of the section proposes an unsupervised method to learn to disentangle the time-varying visual factors from the permanent ones in a large dataset of urban scenes./nPart III discusses one possible avenue for testing whether our man-made systems have achieved human-like rich perception by comparing their performance to that of humans on a unique dataset of abstract art.

Advisor: Efros, Alexei A
Multifactorial Investigation of Perovskite Solar Cell Degradation in Operation

In 2009, Kojima et al. introduced a perovskite solar cell delivering 3.8% power conversion efficiency (PCE). Then, in 2012, Henry Snaith’s group demonstrated a solution-processed perovskite solar cell with a PCE of 10.9%. Ever since, a race for the highest PCE perovskite solar cells has taken over the photovoltaic research community with the latest confirmed maximum PCE, as of 2020, at 25.2%. However, to be realistically viable, perovskites need to overcome their problem with long-term stability. Numerous studies, both theoretical and experimental, have tackled the issue. Some involved looking solely at the effects of humidity, temperature or oxygen on the active layer, whereas others focused on the effects of UV light on the solar cell structure. These single factor studies provide invaluable insights on the degradation of perovskites. Yet, a more holistic approach is needed to understand the effects of salient primary factors along with confounding higher order factors on the degradation. The design of experiment (DOE) methodology is uniquely suited for this endeavor. Few studies have employed a multifactorial approach in perovskite investigations. In this work, we seek to provide a mechanistic understanding of the degradation of perovskite solar cells in operation by focusing on methylammonium lead triiodide (CH₃NH₃PbI₃ or MAPbI₃) and tracking over time its crystallographic (XRD), optical (UV-Vis), and electrical (IV) characteristics under various electric load and temperature conditions. Moreover, we also record the evolution of electronic defects via Photo-Induced Current Transient Spectroscopy (PICTS). Using these techniques, we found that two interaction factors (temperature × load & temperature × time) were significant in the degradation of the perovskite cells studied, which validates the importance of our holistic approach. Furthermore, we found that bands of trap states, initially highly localized deep within the band gap of the perovskite, widened over the exposure period and increasing temperature. We also found that the average trap activation energy for each band of trap states became shallower over the degradation period. These observations establish a mechanistic link between deep level traps and the evolution of the current-voltage characteristics, crystallite size, microstrain, and optical absorption.

Advisor: Subramanian, Vivek
Dubon, Oscar
Aspects of Generalized Entropy And Quantum Null Energy Condition

There is a deep connection between entanglement and geometry in quantum gravity. One manifestation of this connection is through the generalized entropy: the sum of the area of Cauchy-dividing surfaces in Planck units and the von Neumann entropy of the fields on one side of the surface. The generalized entropy is expected to satisfy fundamental thermodynamical conditions which in certain limits result in quantum field theory (QFT) inequalities relating the energy density and the shape derivatives of the von Neumann entropy, most notably the quantum null energy condition (QNEC). This dissertation is devoted to studying various aspect of the generalized entropy, the QNEC, and other information-theoretic aspects of QFT. First, we will use the QNEC to demonstrate the locality of the logarithm of the vacuum density matrix in certain regions of any holographic conformal field theory (CFT). We will then holographically prove the QNEC in general curved spacetime of less than six spacetime dimensions. Next, we will demonstrate a certain limit of the QNEC where an equality between certain components of the stress tensor and the second shape derivative of von Neumann entropy emerges. We will then focus on the study of generalized entropy. Within the AdS/CFT framework, we find a microscopic description for the generalized entropy of black holes using a geometric construction. We then apply this construction to extremal surface in AdS, which constitutes the bulk dual of the Connes cocycle flow on the boundary CFT. Finally, we use propose a novel lower bound on the total energy of spacetime involving the generalized entropy on certain light-sheets.

Advisor: Bousso, Raphael