VictorJuiz/thesis huggingface dataset:
Renewable Polymeric Energy Harvesters from Moisture and Heat

Electricity is indispensable in our daily life and batteries are the widely used energy sources for portable devices in the modern world. However, harmful chemicals and wastes in batteries can lead to serious environmental and health problems without proper dispositions. As such, renewable energy sources such as solar cells have drawn lots of interests with great progresses in recent years. In this work, two forms of renewable energy harvesters based on polymers are studied based on moistures and thermal gradients in the environment. Material properties, fundamental operation mechanisms, and application demonstrations of these two renewable energy harvesters are investigated in the dissertation./nThe moisture-based electric generators have long working time and high energy density output by utilizing the mechanism of the proton transportation and proton concentration oscillation. The specific polymer used in the generator has a moisture-induced oscillation surface potential from the functional groups of the polymer. Both dynamic simulation and oscillation theory have been developed to analyze this phenomenon. The self-oscillation behavior help extending the operation period of the energy harvester under the constant feeding of moistures. Experimental results show the short-circuit current density can reach 1.5 μA/cm2 and a maximum open-circuit voltage of 0.4 V with a long operation period up to 15,000 seconds with an energy density of 16.8 mJ/cm2./nThe thermoelectric energy harvester has an ultra-high Seebeck coefficient and self-healing properties, which is suitable for harvesting energy from temperature gradients such as wearable devices between the human skin and the environment. The energy generator is based on the material of ionogel, which has different pore sizes and polarities to results in the very different diffusion coefficients of anions and cations. Specifically, the diffusion coefficients are characterized and simulated by using the molecular dynamic simulation under different temperatures and tested experimentally. The whole device has a measured Seebeck coefficient at 298 μV/K, which is generally higher than those of traditional thermoelectric materials. Furthermore, the prototype device is flexible, bendable and self-healable for potential applications in harvesting electrical energy for wearable devices.

Advisor: Lin, Liwei
Characterizing the mechanism of cell-cell fusion induced by the bacterium Burkholderia thailandensis

Cell-cell fusion is important for biological processes including fertilization, development, immunity, and microbial pathogenesis. Bacteria in the pseudomallei group of Burkholderia species, including B. thailandensis, spread between host cells by inducing cell-cell fusion. Previous work showed that B. thailandensis -induced cell-cell fusion requires intracellular bacterial motility and a bacterial protein secretion apparatus called the type VI secretion system-5 (T6SS-5), including the T6SS-5 protein VgrG5. However, the cellular level mechanism and T6SS-5 proteins important for bacteria-induced cell-cell fusion remained incompletely described. Using live cell imaging, we found that bacteria used actin-based motility to push on the host cell plasma membrane to form plasma membrane protrusions that extended into neighboring cells. Then, membrane fusion occurred within these membrane protrusions, either proximal to the bacterium at the tip or elsewhere within a protrusion. Expression of VgrG5 by bacteria within membrane protrusions was required to promote cell-cell fusion. Furthermore, a second predicted T6SS-5 protein, TagD5, was also required for cell-cell fusion. In the absence of VgrG5 or TagD5, bacteria in plasma membrane protrusions were engulfed into neighboring cells. Our results suggest that the T6SS-5 effectors VgrG5 and TagD5 are secreted within membrane protrusions and act locally within membrane protrusions to promote membrane fusion. Continued investigation of this pathway will enhance our understanding of the cellular and molecular mechanisms of membrane fusion and cell-cell fusion.

Advisor: Welch, Matthew D
PRECISION TUNING OF SINGLE-METAL SITES ON METAL-ORGANIC FRAMEWORKS FOR CATALYSIS AND GAS ADSORPTION

Metal-organic frameworks (MOFs) are a class of porous crystalline materials created by joining metal ions or clusters, known as nodes, with organic linkers to form a continuous structure. Since the chemical and physical properties of MOFs are highly tunable via the judicious choice of metals and organic linkers, and is further expanded by the possibility for post-synthetic modification, MOFs have over the last decade become a powerful platform for the atomically precise tuning of single-metal sites for applications including gas adsorption and separation, heterogenous catalysis, and chemical sensing etc. In this dissertation, the post-modification of MOF nodes via installation of transition metal catalytic sites, as well as the exploration of guest-induced phase changes that alter the chemical properties of node single-metal sites are explored. First, an artificial enzyme mimic on a solid support was synthesized via the post-synthetic insertion of a bis(µ oxo)dicopper moiety between the clusters of a Zr-MOF (i.e., MOF-565) with a short intercluster distance. Like its parent enzyme, the resulting MOF-565-CuII efficiently transfers one oxygen atom from O2 gas to olefins, forming the monooxygenated epoxide products with high activities and selectivities. The effect of node type and saturation on the post-synthetic installation of iron on zirconium MOFs was then studied. The unsaturated, 8-connected Zr6 nodes of MOF-565, as well as the Zr9 nodes of another new MOF, NPF-520, were furnished with iron(III) to afford MOF-565-FeIII and NPF-520-FeIII, respectively. The visible-light photocatalytic oxidation of toluene to benzaldehyde and/or benzoic acid was then used as a benchmark to compare the Zr-Fe catalysts, both of which demonstrated excellent performance and selectivity under mild reaction conditions. Finally, the effect of reversible guest-induced phase changes of a novel flexible cobalt MOF, Co-MOF, on C2 gas adsorption, in particular ethylene, was investigated. Two means for phase-transformation were identified: 1) the guest-assisted rotation of an asymmetric linker affecting the coordination number of Co and 2) the opening and closing of the MOF structure by activation and re-solvation, changing the coordination geometry of Co.

Advisor: Zhang, Jian
Scattering resonances and the complex absorbing potential method

Scattering resonances are the analogues of eigenvalues for problems on non-compact domains. The real part and imaginary part of the resonances capture the rates of oscillation and decay of the scattering waves. Hence the location of resonances reflects the long-time behavior of the waves on non-compact domains./nIn this thesis we study a computational technique for scattering resonances, that is the method of \emph{complex absorbing potentials} (CAP). We show that the CAP method for computing resonances applies to the case of scattering by exponentially decaying potentials. We also show that the CAP method is valid for an abstractly defined class of \emph{black box} perturbations of dilation analytic second order differential operators which is close to the Laplacian near infinity. The black box formalism allows a unifyingtreatment of diverse problems ranging from obstacle scattering to scattering on finite volume surfaces without addressing the details of specific situations./nThe black box scattering problem motivates us to study the boundary perturbations in obstacle scattering. We show that all resonances in obstacle scattering with Dirichlet boundary condition are generically simple in the class of obstacles with $C^k$ (and $C^\infty$) boundaries, $k \geq 2$. This generalizes the case of eigenvalues of second order elliptic operators on a compact domain that all eigenvalues are simple for a generic compact domain.

Advisor: Zworski, Maciej R
Methods for High-throughput, Cellular-Resolution Estimation of Synaptic Properties

Circuit-mapping experiments combining whole-cell electrophysiology with two-photonoptical stimulation of potentially presynaptic neurons (“2p-mapping”) have produced rich data on monosynaptic connectivity of neural circuits. However, mapping densely- packed presynaptic populations at cellular resolution has proven challenging, making the precise localization and identity of connected neurons difficult. To interpret data resulting from these experiments, it is therefore critical to develop statistical methods which can infer the properties of neural circuits despite the limited spatial resolution of state-of-the-art 2p-mapping technologies. In this work, we present several novel statistical methods for mapping neural circuits at cellular resolution. In Chapter 1, we present background on mapping neural circuits and the methods used. In Chap- ter 2, we present a novel statistical method for inferring monosynaptic connections when the resolution of two-photon stimulation is not sufficient given the density of the opsin-expressing cells. In Chapter 3, we present a high-resolution approach for infer- ring common input to two simultaneously patched neurons during mapping. And in Chapter 4, we present a high accuracy method for inferring the timing of post-synaptic currents - the key response observation when mapping neural circuits.

Advisor: Adesnik, Hillel
Deep learning applications in wildlife recognition

Deep learning has attracted much attention from the ecological community for its capability of extracting and generalizing patterns from data sets with highly complicated structures, such as images, audios, and motion signals. However, despite the promising cases, deep learning is complicated in terms of application and has shortcomings when applied to real-world ecological data sets. In this dissertation, we focus on: 1) demystifying the hidden mechanisms of deep learning in terms of wildlife recognition, 2) identifying the challenges of deep learning applications in wildlife recognition, and 3) proposing a generic recognition framework that can be practically deployed in the fields./nIn the first chapter, we examine how deep learning recognizes wildlife through Convolutional Neural Network feature deconstruction and interpretation. The objective is to demystify aspects of artificial intelligence and facilitate wildlife recognition research./nThe second chapter identifies three major challenges to automatic wildlife recognition through an avian recognition case study and provides preliminary solutions addressing each challenge. This chapter aims to increase awareness in the ecological community of these challenges, bridge the gap between ecological applications and state-of-the-art computer science, and open doors to future research./nIn the third chapter, we propose a hybrid recognition system of machine learning and human-in-the-loop that overcomes two challenges discussed in the second chapter: imbalanced data distribution and continuous data expansion. Moreover, with the self-updating mechanism of our approach, the system can be practically deployed in the fields.

Advisor: Getz, Wayne M
Yu, Stella X
Advances in nearly-magnetic superconductivity

Many-body systems present some of the deepest challenges in physics. The interactions between a large number of objects can give rise to phenomena which are unprecedented from the standpoint of the individual objects themsleves. In many-body quantum systems, such as the electron fluid in metals, an effective single-particle description for modeling their properties has been successfully developed and tested. One of the central open questions in the study of many-body quantum physics lies in systems where the particles interact with each other strongly enough that the conventional description in terms of effectively individual particles fails. This problem is prevalent in metals which are on the verge of magnetism -- for example, when perturbed by external stress, magnetic field, or electric field, they spontaneously become magnetic. Notably, some of these metals achieve superconductivity, a collective electron phase which exhibits zero electrical resistance, at remarkably high temperatures. This text focuses on experimentally addressing fundamental open questions regarding strongly-interacting electrons in such metals. It is divided into separate sections on nearly-antiferromagnetic iron-based superconductors, a novel layered ferromagnetic superconducting compound, and a cerium-based alloy hosting an interplay between magnetism and the spontaneous localization of electrons to their host atoms.

Advisor: Analytis, James G
Challenges of Control Barrier Functions: Optimization, Control, Planning, and Navigation

Control barrier functions (CBFs) are one of the many used approaches for achieving safety in robot autonomy. This thesis tackles several challenges present in control barrier functions in different aspects, including optimization, control, planning and navigation./nThis thesis is composed of three parts. In Part I, we point out the optimization infeasibility between CBF constraint and input constraint, and address the feasibility problem in optimal control for quadratic programming using control barrier functions under input constraints. We also notice that the potential conflict between input constraints and safety constraints also exists in the discrete-time domain together with model predictive control. This conflict is formally identified in reachability analysis and reachability is later enhanced in proposed formulations./nIn the Part II, we focus on obstacle avoidance for control and trajectory generation in a tight environment, which requires polytopic obstacle avoidance. We analyze the optimization problem for obstacle avoidance between polytopes. The novel optimizations for obstacle avoidance in continuous domain and discrete-time domain are proposed to solve this challenge./nIn Part III of the thesis, we discuss several applications of motion planning and navigation using control barrier functions.We firstly address the motion planning problem within a finite state machine which unifies a mid-level planner and a low-level safety-critical controller with application to autonomous driving. Next, we propose parallelism for motion planning with control barrier functions with application to autonomous racing. Finally, we explore the possibility of safety-critical motion planning for high dimensional systems such as Cassie, a life-size bipedal robot.

Advisor: Sreenath, Koushil
Multiphoton Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) consists of three main components: a main static magnetic field for producing magnetization in a sample or subject, spatially varying magnetic fields for spatial encoding of the magnetization, and radiofrequency (RF) magnetic fields for manipulating the magnetization. This dissertation is mainly concerned with the RF fields. The frequency of the RF fields is set to the resonance determined by the strength of the main magnetic field and the properties of the object being imaged. For the usual case of imaging Hydrogen nuclei, the frequency is on the order of hundreds of MHz on superconducting clinical MRI scanners. While standard MRI only uses RF near the resonance frequency, we propose the use of multiphoton excitation in MRI, where multiple RF fields whose frequency sums or differences equal the resonance frequency are used instead. No RF at the resonance frequency is required./nWe begin by verifying multiphoton resonances in MRI via simulation and experiment. Practically, this is done using a combination of RF in the kHz and RF in the MHz, for example 30 kHz and 127.71 MHz for a 127.74 MHz resonance frequency. While multiphoton excitation with RF at a single subharmonic frequency, such as with half-resonance frequency RF at 63.87 MHz for a 127.74 MHz resonance, is possible, it is not practical for human scale MRI due to excessive power requirements. Although the RF fields in MRI are often praised for being safe in that they are non-ionizing, they still pose the danger of potentially excessively heating a subject./nBy using RF in the kHz and the MHz, the power efficiency of multiphoton excitation is greatly increased, and the kHz RF provides negligible additional power deposition in the human body. By taking advantage of this fact, we can actually reduce overall power deposition in the body in more spatially complex excitation patterns such as multiband excitation by simultaneously making use of multiple multiphoton resonance conditions at different spatial locations. With the addition of power-deposition-negligible kHz RF, the power-deposition intensive MHz RF can be reused for multiple excitations at different spatial locations instead of just one spatial location./nNext we move onto providing a more rigorous analysis of spatially selective excitation with multiphoton resonances. With this analysis we see that amplitude and frequency modulation can be traded-off between the different RF fields in a multiphoton resonance, granting additional flexibility. After demonstrating this additional flexibility with a homemade extra kHz-range RF coil, we also show its practicality on an in vivo human head. As future MRI systems increasingly gain complexity, we believe this additional flexibility will enable novel applications.

Advisor: Liu, Chunlei
RDMA-Based Distributed Data Structures for Large-Scale Parallel Systems

Running programs across multiple nodes in a cluster of networked computers, such as in a supercomputer or commodity datacenter system,is increasingly important across multiple domains, including data science, machine learning, and scientific computing. This is brought on by a combination of increasing data sizes, which push beyond the memory capacity of a single node, and increasing computational demands from new, more elaborate simulations, models, and applications./nHowever, writing parallel programs for clusters of computers remains a difficult task, particularly for programs that are irregular in terms ofdata distribution or access pattern. Many parallel programs today are still written using communication libraries like MPI or OpenSHMEM, which require users to explicitly manage low-level details. While high-level parallel programming languages and libraries do exist, and these can make implementing certain types of programs much easier, developers often have to expend significant effort building custom infrastructure and data structures for their applications./nThis thesis argues that a large part of the reason why parallel programming remains difficult is a lack of high-level distributed data structures analogous to the data structures that have become ubiquitous in sequential programming environments like C++ and Python. These especially include irregular data structures like hash tables and queues that may require fine-grained memory accesses along with synchronization. This thesis examines techniques for building high-level, cross-platform distributed data structures using one-sided remote memory operations like remote put, remote get, and remote atomics. These memory access primitives allow for a high degree of asynchrony, enabling better performance by removing synchronization bottlenecks and allowing a high degree of overlap between communication and computation. They can also be efficiently executed directly by the network hardware in modern supercomputer and commodity datacenter networks, removing the need to synchronize with remote processes./nThis thesis examines several RDMA-based distributed data structures, including hash tables, Bloom filters, queues, and dense and sparse matrices. We provide a performance model for evaluating the cost of RDMA-based distributed data structure methods in terms of their component remote memory operations, and demonstrate how this model can be extended to support GPUs in addition to conventional CPUs.

Advisor: Yelick, Katherine
Buluç, Aydın
Horizontal-strip LLT polynomials

Lascoux, Leclerc, and Thibon defined a remarkable family of symmetric functions that are q-deformations of products of skew Schur functions. These LLT polynomials G_\lambda(x;q) can be indexed by a tuple \lambda of skew diagrams. When each skew diagram is a row, we define a weighted graph \Pi(\lambda). We show that a horizontal-strip LLT polynomial is determined by this weighted graph. When \Pi(\lambda) has no triangles, we establish a combinatorial Schur expansion of G_\lambda(x;q). We also explore a connection to extended chromatic symmetric functions.

Advisor: Haiman, Mark
Automated Detection of Social Signals Using Acoustic and Lexical Features of Extemporaneous Speech in Naturalistic Environments

The goal of this dissertation is to study and develop approaches to automating detection of social signals from speech using extemporaneous talk gathered in naturalistic settings. All three chapters focus on detection of what is referred to as competence-focused and likability-focused speech, which are two examples of social stances that humans advance in social interactions and that may be detected by machines./nThe first chapter describes the development and performance of an approach to detecting competence-focused and likability-focused speech among expert speakers, namely, professional actors and voice-over experts. I demonstrate that speakers’ attempts to advance such social stances can be detected with a level of accuracy that approximates an existing benchmark. The second chapter follows a similar design and approach but uses instead a corpus of audio recordings collected from non-expert speakers—participants who do not have training or experience as actors./nThe first and second chapters describe models that were developed and tested to use the acoustic features of recorded speech to infer whether the speaker was responding to a social situation and directive that prompted competence-focused speech or likability-focused speech. In those cases, the classification problem required an inference about the stimulus that prompted the speaker. There is also merit in inferring how a human interlocutor would perceive a given sample of speech, an example of a general type of problem that has been termed inferential detection. Inferential detectors utilize machine learning and measurement processes to infer human judgements of objects, agents, processes, or environments even in the absence of a human observer. The third chapter presents a general process for developing inferential detectors of social stances. In addition, I develop and describe inferential detectors for competence-focused and likability-focused speech that utilize multiple sources of information about the speaker, in this case, acoustic features of speech as well as its lexical content.

Advisor: Wilson, Mark