VictorJuiz/thesis huggingface dataset:
Crowd Note
Keywords: self-directed learning
collaborative learning
online learning
Thesis

Today online self-directed learning has become a common approach for learning due to the ubiquity of computers with internet access. Self-directed learning is especially prevailing in domains with significant social needs, such as Computer Science. However, learners often find it difficult to resolve their confusion during learning and feel isolated. Utilizing the connectivity of online environments and grounded in the theory of Community of Practice, this project proposes Crowd Note, an interactive platform that leverages collaborative efforts during online video-based asynchronous learning for university-level learners. The tool has two main design elements – a video-integrated discussion sidebar and a crowd-based concept map. A pilot study was conducted with two experiments of 49 participants. It is found that the tool raised learning engagement and learning gain moderately. Though there are variations across learners, it helped most learners feel more socially connected. It also promoted learners to use self-explanation learning strategy to better understand what they were taught. Through this project, we demonstrate the promising future of self-directed learning by designing and creating a collective community in an online learning environment.
Crystal domain mapping with polarized raman spectroscopy and Chi-(2) nonlinear optics in CMOS waveguides
Keywords: Electrical Engineering.

Polarized Raman spectra collected from a diffraction-limited spatial volume constitute a versatile scanning probe. Polarization selection rules for Raman scattering in crystals provide a signature of local symmetry and orientation. Making use of this, I demonstrate orientation-resolved domain mapping in tetragonal strontium titanate (SrTiO3) and discuss the relevance of this technique to the study of electronic materials exhibiting emergent spatial inhomogeneity. In Second-order nonlinear optical susceptibility is forbidden to dipole order in inversion-symmetric media, however this rule can be broken with an applied DC electric field. Historically known as electric field induced second harmonic (EFISH) generation, this effect can be understood as four-wave mixing between three optical electric fields and one DC electric field. The dense integration of microelectronic devices with optical waveguides available in CMOS photonics enables strong EFISH using relatively modest voltages and periodically-poled DC electric fields for quasi-phase- matched three-wave mixing. I will discuss device design and characterization efforts aimed at developing on-chip optical parametric oscillators and up-conversion detectors for mid- infrared light.
Cyber risk management :AI-generated warnings of threats
Keywords: Management Science and Engineering.

This research presents a warning systems model in which early-stage cyber threat signals are generated using machine learning and artificial intelligence (AI) techniques. Cybersecurity is most often, in practice, reactive. Based on the manual forensics of machine-generated data by humans, security efforts only begin after a loss has taken place. The current security paradigm can be significantly improved. Cyber-threat behaviors can be modeled as a set of discrete, observable steps called a 'kill chain.' Data produced from observing early kill chain steps can support the automation of manual defensive responses before an attack causes losses. However, early AI-based approaches to cybersecurity have been sensitive to exploitation and overly burdensome false positive rates resulting in low adoption and low trust from human experts. To address the problem, this research presents a collaborative decision paradigm with machines making low-impact/high-confidence decisions based on human risk preferences and uncertainty thresholds. Human experts only evaluate signals generated by the AI when decisions exceed these thresholds. This approach unifies core concepts from the disciplines of decision analysis and machine learning by creating a super-agent. An early warning system using these techniques has the potential to avoid more severe downstream consequences by disrupting threats at the beginning of the kill chain.
The dark matter radio : a quantum-enhanced search for QCD axion dark matter
Keywords: Physics.

This dissertation introduces the Dark Matter (DM) Radio, a tunable, lumped-LC oscillator search for axion and hidden-photon dark matter between 1 peV and 1 ueV. The design of DM Radio is motivated from fundamental statements regarding impedance-matching to dark matter and optimal electromagnetic searches for axions and hidden photons subject to the Standard Quantum Limit (SQL) on phase-insensitive amplification. I describe the DM Radio Pathfinder, a prototype detector searching for hidden-photon dark matter between 400 peV and 40 neV. First data from the Pathfinder are presented. I propose the Radio-Frequency Quantum Upconverter (RQU), a device which enables DM Radio readout sensitivity beyond the SQL. The ultimate vision for DM Radio, a cubic-meter-volume resonant experiment operating at 10 mK with a 4 Tesla DC magnetic field and exploiting quantum metrology, is presented. This experiment will enable a probe of the QCD axion between 4 neV and 1 ueV.
Data for decarbonization: Emissions and flexibility findings from data-driven thermal generator analysis
Keywords: School of Earth Energy &amp; Environmental Sciences
grid
decarbonization
data-driven
Thesis

The electric grid is transforming more quickly than ever before, from centralized to decentralized, dispatchable to variable, and fossil to renewable. And yet climate change necessitates even faster decarbonization. This thesis formulates data-driven methods for analyzing, improving, and decarbonizing the electric grid. Methods are applied to the Western Interconnection (WI), but can be used in any electricity system.
First, major trends in emissions and operations are visualized and contextualized with policy and market background. Market-based policy and cheap natural gas drove down SO2 and NOx emissions over the last two decades. CO2 is not priced, except in California, and has see weak reductions over the same period. Second, data-driven methods are developed to find minimum load (Pmin), maximum load (Pmax), and maximum ramp rates (Ru, Rd) for thermal generating units (TGU). These parameters explain TGU flexibility, which is critical to renewable integration, Comparison to standard values reveals substantially more existing TGU flexibility, in the form of lower minimum load and proven ramp rates. This finding uncovers an extremely rapid and low-cost way to add power system flexibility: incorporating data-driven TGU parameters into grid operation models. Third, the impact of variable renewable electricity (VRE) on carbon intensity (CI) of TGUs is quantitatively assessed. The impact so far has been small, but impacts could increase if TGUs spent significant amounts of time operating at low load factors. Findings suggest that our understanding of the grid could be improved through novel data-driven analysis of emissions trends, operations trends, TGU parameters, and system impacts of VRE. These analyses clarify a path toward decarbonized electricity.
Decarbonizing transportation : electric vehicle adoption and emission impacts
Keywords: Management Science and Engineering.

This dissertation examines the potential for electric vehicles to act as a viable technology pathway to deep decarbonization of the transportation sector. Taking an integrative and multi-disciplinary approach, this research applies theories of technology development and diffusion, social welfare optimization, life cycle analysis, and empirical modeling to better understand the processes and prospects for widespread adoption, as well as the emissions benefits that could accrue from a full market transition. The first chapter develops an endogenous model of market diffusion which incorporates positive feedback effects such as learning-by-doing and network externalities, then uses it to consider what an optimal subsidy policy regime could look like in different representative scenarios. The next chapter goes more in-depth on emissions, presenting the first comparative full life cycle analysis of greenhouse gas emissions for mass market, long-range battery electric vehicles, as compared to internal combustion engine vehicles. Lastly, data from real-world trips are utilized to explore heterogeneity in vehicle efficiency and range under different trip conditions, factors which could have significant implications for the scalability of the electric vehicle technology. Broadly, results show very large electric vehicle emissions reductions across all markets, robustness of key performance metrics to local climate conditions, and positive social welfare impacts of subsidies from accelerating early market adoption.
Decorative cut glass and the working class in America, 1876 to 1916
Keywords: Art and Art History.

My project develops a social art history of a once-popular genre of decorative art called "cut glass, " domestic glassworks like bowls and vases incised with geometric patterns against stone and metal wheels. Specifically, I consider how the medium, its widely-discussed manufacture, and representations of each intervened in how working-class citizens created and negotiated their perceptions of themselves, their compatriots, and their nation. Scholarship on cut glass privileges the stories of upper- and middle-class consumers, but wage laborers—including those who made and maintained cut glass—encountered cut glass, its marketing, and their attendant class biases as well. Over five object-centered chapters, I show how public demonstrations of glass cutting, illustrations of domestics with cut glass, President McKinley's much-publicized punch set, and other artifacts reinforced and, often, unintentionally challenged prevailing conceptions of social class, privilege, and mobility. Given what one period journalist called "the rage for cut glass, " the medium offers a privileged site for apprehending the intersections of class, labor, and materiality in Gilded Age America, and their implications for working-class life and culture.
Deep learning for understanding dynamic visual data
Keywords: Electrical Engineering.

Teaching machines to interpret the visual observations of our dynamic world as humans do is a central topic in Artificial Intelligence. The goal is to process various types of visual data and generate symbolic or numerical descriptions similar to human understanding to support decision making of autonomous agents. Compared to an individual visual snapshot, a dynamic visual data sequence accumulates more relevant information over time, allows motion information to be leveraged, and therefore potentially enables better generation of such descriptions. The recent success of deep learning inspires us to utilize deep neural networks to analyze the complex patterns of dynamic visual data, in contrast to traditional approaches which rely on hand-crafted spatiotemporal descriptors. Different from previous related deep learning methods, in this thesis, we argue that the correspondences of positions across frames are the dynamic component of visual data and should be modeled by the deep network architectures. We discuss the design philosophies for the deep architecture in terms of selecting correspondence candidates, generating representations from the candidates through learning, and deploying the network to various applications. Accordingly, we present four deep learning methods for processing and understanding dynamic visual data. The processed visual data modality covers two or multiple frames of 2D RGB images or 3D point clouds. We start by introducing FlowNet3D, a deep neural network for estimating scene flow between point clouds at consecutive timestamps in an end-to-end fashion. Our method lets points in one point cloud find correspondence candidates in another point cloud to learn the true correspondences and shows great advantages while being evaluated on existing benchmarks. We then present CPNet and MeteorNet, two deep learning backbone architectures that learn representations for RGB videos and 3D point cloud sequences respectively. Both methods effectively learns temporal relations by proposing and aggregating correspondence candidates. We showcase their leading performance on tasks including action recognition, semantic segmentation and scene flow estimation. We also describe KeyPose, a deep learning architecture for estimating 3D keypoint locations of objects from stereo RGB images, as well as a new dataset for studying transparent objects. Through extensive experiments, we demonstrate that estimating 3D object poses by modeling correspondences in stereo images has advantage over depth-based methods. This thesis concludes with a discussion on other potential application domains and directions for future research
Deep neural networks for 3D protein structural analysis
Keywords: Bioengineering.

Central to protein biology is the understanding of how structural elements give rise to observed function. The surfeit of protein structural data enables development of computational methods to systematically derive rules governing structural-functional relationships. However, performance of these methods depends critically on the choice of protein structural representation. Most current methods rely on features that are manually selected based on knowledge about protein structures. These are often general-purpose but not optimized for the specific application of interest. In this dissertation, I present a general framework that applies deep neural networks to structure-based protein analysis. The framework automatically extracts task-specific features from raw atom distributions or simple descriptors, driven by supervised labels. As a pilot study, I applied 3D convolutional neural networks (3DCNNs) to analyze the 20 amino acid microenvironments and predict the amino acids most compatible with a given location in a protein. Our 3DCNN achieves a two-fold increase in prediction accuracy compared to models that employ conventional engineered features and successfully recapitulates known information about similar and different microenvironments. I further applied the 3DCNN framework to detecting protein functional sites and demonstrated that 3DCNNs performed better on site detection tasks compared to recently published methods. Finally, I propose a graph-convolutional (Graph-CNN) framework for predicting protein-ligand interactions and show that Graph-CNNs achieved better or comparable performance to recent methods on virtual screening tasks, without relying on protein-ligand co-complexes. End-to-end trained deep learning networks consistently outperform methods using hand-engineered features on various important biological tasks, suggesting that the 3DCNN and Graph-CNN frameworks are well suited for protein structural analysis and hold great promise for advancing our understanding in protein science and drug discovery.
Deep object-centric 3D perception
Keywords: Electrical Engineering.

Teaching machines to perceive visual content in a 3D environment as humans do is a central topic in Artificial Intelligence. The goal is to be able to process different types of 3D sensory inputs and generate symbolic or numerical descriptions about the environment to support decision making. In this thesis, we advocate an object-centric way to generate such descriptions, in which we represent an environment as a collection of 3D objects equipped with various attributes important for specific tasks. To generate such a representation, we focus on deep object-centric 3D perception, a class of approach built upon 3D deep learning techniques. This thesis covers three critical components of deep object-centric 3D perception: constructing large-scale 3D model repository, designing 3D deep learning frameworks to consume various formats of 3D data, applying big data and deep learning techniques to real perception tasks. We start by providing an overview of each component. Following this, we show how we could accelerate the labeling acquisition process to scale-up 3D model repositories so that data-hungry deep learning approaches can be applied. 3D data can usually be represented in different formats. Some of the prevalent geometric formats, such as point cloud and polygon mesh, poses a significant challenge to deep learning framework design since traditional deep nets designed for regular data forms, e.g., images, can not be directly applied. We then investigate how to build deep learning frameworks capable of consuming 3D shape meshes, an irregular graph-structured data format. Next, we provide two real perception applications as case studies, to show how big data and 3D deep learning help the field evolve. In particular, we study instance segmentation in 3D point cloud and develop a novel 3D object proposal network named GSPN as well as a 3D instance segmentation framework named R-PointNet, which boosts the state-of-the-art instance segmentation performance by a large margin on existing benchmarks. In the second application, we go one step further and tackle detailed part-level perception. We study the problem of articulation-based object part segmentation. We show how to modularize deep network design by disentangling complex perception problems into subproblems. We conclude by summarizing our efforts and discuss the challenges and open questions in the field.
Deep optics : optimizing integrated imaging and computer vision systems
Keywords: Bioengineering.

Over the past decade, computational imaging has transformed the expectations we have for our cameras. At the fingertips of anyone with an iPhone X, for example, are panoramas, high dynamic range (HDR) photography, and Portrait mode---all classic examples of computational imaging. This integration of optical design and image processing has also enabled us to see around corners and behind walls, observe molecular dynamics during chemical reactions, and even to piece together the first image of a black hole. For each application though, the system needs to be reformulated and designed, and it is difficult to claim that the final solution is actually optimal. Concurrently, a tremendous amount of progress has been made in the field of deep learning. Convolutional neural networks (CNNs) have found success in a wide variety of computer vision tasks, including image classification, object detection, image generation, and more. However, this high performance comes at a high computational cost, and it remains difficult to deploy CNNs in embedded systems due to tight power budgets. Through earlier projects discussed in Section I, we gain an understanding of the ways computational imaging and deep learning could potentially enhance and benefit from the other. In section II, we present the concept of deep optics, in which the optical model is jointly optimized with the task-specific algorithm. Deep optics capitalizes on the recent successes of deep learning for computer vision and uses the same data-driven approach to optimize optical parameters together with subsequent neural network parameters. Not only could this improve computer vision performance over basic or heuristically designed cameras, but it could also relieve some of the computational cost by partially pre-processing the data in optics. We begin by building a hybrid optical-electronic convolutional neural network for image classification, specifically focusing on the concept of an optimizable optical convolutional layer to replace the standard electronic convolutional layer. Finally, we apply deep optics to the problems of monocular depth estimation and 3D object detection, demonstrating how these common computer vision tasks that typically ignore the image capture process can be improved by proper modeling and optimization of the optics.
Deformations of generalized Fuchsian representations
Keywords: Mathematics.

This thesis studies the deformation theory of surface group representations in to SL(4, R) -- in particular, those near what we term the generalized Fuchsian locus. We show that deformations of these representations admit geodesic laminations on the boundary of the convex hull of the limit set in RP^3, which generalizes the classical theory of quasi-Fuchsian deformations. In this new setting, however, we show that there exist such deformations for which this lamination supports no transverse measure, which cannot happen for hyperbolic deformations.
Deliberation and attitude changes : examining the effects of encountering diverse opinions
Keywords: Communication.

The collection of essays in this dissertation examines the underlying mechanisms of deliberation on attitude changes at the individual level. In the first study, I focus on two important variables in deliberation groups: opinion heterogeneity and opinion difference to examine whether and how deliberating with dissimilar views leads to opinion changes. With the data from the Deliberative Polls in Europe and Korea, my analyses show that: (1) opinion heterogeneity and opinion difference within groups are positively associated with opinion changes; (2) although the pre-deliberation group mean opinions significantly affect individuals' post-deliberation opinions, the opinion heterogeneity significantly reduce such group influences on post-deliberation opinions; (3) participants with more extreme attitudes before deliberation tend to be more easily influenced by the dissimilar views. In the second study, combining the theories on ideological identifications with the promise of deliberative practices in promoting informed political decision, I examine the extent to which ideological identifications influence policy attitudes in deliberative settings. I analyzed the Deliberative Poll data from Australian Referendum and EuroPolis 2009, which included deliberation groups and control groups. The results show that: (1) individuals' ideological identification exerts significant influences on political attitudes for both deliberation participants and nonparticipants; (2) deliberation significantly enhances the influence of ideological identifications on issue attitudes. It suggests that deliberation enhances the consistency between ideological identification and issue attitudes; (3) attitudes changes show different patterns depending on the participants' ideological identifications. The moderates who do not have strong ideological identifications changed attitudes the most, while the liberals and conservatives did not show consistent significant attitude changes in deliberation. In the third study, based on the theories of Elaboration Likelihood Models (Petty & Cacioppo, 1981; 1986), I examine whether deliberation leads to polarization at the individual level. I analyzed the Deliberative Poll data from Europe and Denmark. This study finds that: (1) although individuals' attitudes have significantly changed after deliberation, there is no significant evidence for attitude polarization at the individual level; (2) pre-deliberation extremity negatively associated with polarization. That is, deliberation help participants with extreme opinion become moderate. It suggests that the deliberation may help individuals avoid biased elaboration; (3) the heterogeneity of opinions within discussion groups significantly reduces polarization at the individual level; (4) however, the group influence on polarization still exists. When group mean attitudes are more extreme than a participant's attitude before deliberation, his/her attitudes would go more extreme after deliberation.