VictorJuiz/thesis huggingface dataset:
Topological and edge transport in two-dimensional electron systems

Topological insulators host topologically-protected electronic states at their two-dimensional surface. This dissertation will use low-temperature electronic transport measurements to explore basic phenomenology and several applications of these topologically-protected surface states in topological insulators and magnetically-doped topological insulators. First, it reports on the quantum anomalous Hall effect, its applications in electrical resistance and current metrology, and the microscopic details of current flow. While topological surface states are robust against disorder, the topological insulators themselves are quite inhomogeneous and fragile, so a discussion of disorder and fabrication-induced damage to these materials is presented. Next, this dissertation discusses two more elaborate types of devices incorporating topological materials: microwave devices, aimed towards developing dissipationless, passive, nonreciprocal high-frequency circuit elements, and Josephson junctions, where topological weak links provide a platform for exploring Majorana physics. Finally, this dissertation reports on measurements of a disordered thin film of a new magnetic topological insulator, where universal conductance fluctuations with a dependence on the details of magnetic domain reconfiguration are identified.

Keywords: Engineering.
Materials Science and Engineering.
Advisor: Goldhaber-Gordon David
McIntyre Paul
Mannix Andrew
Toward point-of-care molecular quantification : novel assays and detection strategies for biosensing in complex media

Molecular quantification at the point-of-care offers a pathway toward personalized and accessible medicine. Rapid characterization of varying biomarker concentrations can enable timely intervention and preventative care. However, in order to function at the point-of-care, biosensors must perform rapidly and directly in complex biofluids such as blood. These biofluids create challenging environments, frequently causing biofouling, signal degradation, and nonspecific binding on the sensor surface. Overcoming these challenges requires innovation in both assay design and detection hardware in order to develop truly point-of-care devices. In this thesis, I first discuss three major objectives of point-of-care devices - (1) an actionable time-to-answer, (2) the ability to perform directly in patient samples, and (3) minimal patient inconvenience. Through this lens, I discuss the development of both custom hardware and novel fluorescent assays to enable rapid molecular quantification directly in complex media. I begin by presenting a custom fiber optic-based biosensor which leverages evanescent sensing to selectively interrogate the surface of the fiber probe, minimizing interference from the background biofluid. I then present two projects applying this biosensor to the detection of two classes of biomarkers. In the first project, I discuss the adaptation of existing aptamers, single stranded nucleic acid sequences that bind to target molecules, into fluorescently-labeled switches to enable continuous monitoring of small molecules. I then present the use of these aptamer switches in conjunction with our custom biosensor to continuously detect dopamine and cortisol in artificial cerebrospinal fluid and undiluted plasma, respectively. In the second project, I present the development of a generalizable fluorescent substrate architecture to detect enzymatic cleavage. This architecture has been coupled with our sensor to rapidly detect the activity of Factor Xa, an enzyme in the coagulation pathway, directly in whole human blood. Following this, I present a third project in which a triple-binding immunoassay is amplified by Hybridization Chain Reaction, a DNA self-assembly reaction, to establish a high signal to noise ratio assay. I discuss the potential optimization and integration of this amplified immunoassay with our fiber-based biosensor to enable rapid, low abundance protein detection at the point-of-care. Finally, I present a proof-of-concept work that makes use of 3D-printed scaffolding to pattern a hydrogel filter, allowing for the development of a point-of-care device that allows for filtration of target molecules out of complex media prior to optical detection. Together, these projects demonstrate a significant advance toward the ability to rapidly quantify biomarkers directly in complex media, providing a promising path toward more generalized point-of-care biosensing.

Keywords: Engineering.
Electrical Engineering.
Advisor: Soh H. Tom
Appel Eric (Eric Andrew)
Vuckovic Jelena
Toward the next generation of GPS signals : new codes and navigation security

Today, billions of users rely on GPS on a daily basis. Woven into the fabric of modern civilization, GPS underlies nearly every aspect of society's functions, including our transportation, agriculture, financial transactions, energy grid, and communication systems. Yet, despite the critical importance of GPS, its civilian signals currently (1) are open and unencrypted, meaning that an attacker can generate counterfeit GPS signals and spoof a user's position or timing solution, and (2) use codes which were designed before the recent advancements in computing. However, in 2024, the NTS-3 satellite will be launched to test new signal capabilities for the future generations of GPS, including a new digital watermark called Chimera to periodically authenticate one's received GPS signal. Furthermore, starting launches in 2026, the GPS IIIF satellites will be the first to have a fully-digital, reprogrammable payload, allowing for new opportunities to re-explore the design of the GPS signal, including their underlying codes. In this dissertation, we seek to leverage these upcoming opportunities in order to advance the next-generation signal capabilities. In particular, we develop strategies to utilize Chimera and additional self-contained sensors onboard a vehicle, such as an inertial measurement unit or wheel encoder, in order to perform continuous spoofing detection and secure, attack-resilient navigation. For our continuous spoofing detector, we use stochastic reachability analysis to conservatively model the error distributions from the self-contained sensor and GPS measurements, in order to provably satisfy a user-defined false alarm guarantee. To perform continuous, attack-resilient navigation, we further leverage the self-contained sensors and Chimera to determine how much to rely on the received GPS measurements, in order to strategically improve real-time navigation performance while mitigating any induced errors during an experienced attack. With the forthcoming ability to reprogram the GPS signals, this dissertation further proposes a new framework to design the GPS codes, leveraging stochastic optimization methods as well as present-day computational tools. To design the GPS codes within the discrete, exponentially large space of binary code sets, we utilize a natural evolution strategy to optimize a smooth probability distribution over the code space, allowing us to evaluate a gradient estimate and leverage state-of-the-art first-order optimization algorithms for code design. We demonstrate that the framework proposed in this dissertation designs codes which have lower self- and inter-signal interference, through reduced mean-squared correlation sidelobes, than competitive sets of Gold and Weil codes, which are code families used by GPS today.

Keywords: Engineering.
Electrical Engineering.
Advisor: Gao Grace X. (Grace Xingxin)
Van Roy Benjamin
Zebker Howard
Towards a novel lesioning method to causally study neuronal population dynamics underlying motor control

Systems neuroscience currently lacks both a mechanistic understanding of how neuronal activity drives behavior and high-spatiotemporal-resolution insight into how the brain responds when neurons are lost due to disease or injury. While there is some knowledge of the correlation between loss of neurons and changes in functional behavior, to date, there is a limited understanding of the changes in neuronal activity in response to loss of neurons and subsequent recovery, which also limits the ability to develop a causal explanation of the relationship between neuronal activity and behavior. This dissertation takes a step towards addressing these outstanding questions by introducing a novel causal investigation method, neuroelectrophysiology-compatible electrolytic lesioning, which allows for both direct inactivation of neuronal activity and the collection of electrophysiology on physiologically relevant timescales for adaptation and recovery. This method was used successfully to create several lesions in two awake-behaving animals, removing neurons from cortex to affect both neuronal activity and behavioral performance on a skilled reaching task. These minor, heterogeneous behavioral effects last for days to weeks before the animal recovers back to baseline behavioral performance. In order to understand the changes taking place in the neuronal activity after lesioning and also as the animal behaviorally recovers, this dissertation explores analyses at two levels of supervision. A supervised offline decoding analysis uses changes in decoding performance to uncover aspects of neuronal population activity that remain or are changed after lesioning. An unsupervised analysis develops two information theoretic metrics that can be calculated on high-dimensional data -- one measure of complexity and one of efficiency at the action potential level -- and evaluates their effectiveness as biomarkers of both loss of neurons and behavioral deficit. Ultimately, results from these studies may be applied to develop assistive brain-computer interfaces that are robust to neuronal loss or to advance rehabilitation methods for diseases that lead to neuronal loss, including stroke and Alzheimer's disease.

Keywords: Engineering.
Electrical Engineering.
Advisor: Nuyujukian Paul
Nishimura Dwight
Weissman Tsachy
Towards AI-driven networks : hardware and software for data-plane ML

Network management is an increasingly difficult task for researchers and industry alike. Networks are growing rapidly in both scale and complexity. They now have to cater to a bigger application set and a larger user base than ever before while adhering to more and more stringent performance requirements. With so many challenges to running a network, operators must move beyond the era of hand-tuned algorithms and instead, adopt more automated approaches, i.e. AI-driven networks. In the search for more versatile tools in networks, many researchers have looked to machine learning (ML) as a vehicle for data-driven, adaptive mechanisms in networking systems. However, a number of pragmatic issues have plagued such development. Can we run ML in the packet path? Must operators build each new ML model by hand? How can we incorporate new data? In this dissertation, we show the construction of integral components required to build AI-driven networks. We first describe the design of Taurus, a platform to enable data-plane ML to run in the packet path of the network with a per-packet granularity, at line-rate. Furthermore, we demonstrate that the hardware for Taurus adds minimal overhead, less than 4% chip area and less than 3% power in our prototype. Next, we discuss Homunculus, a compiler stack for data-plane ML platforms (like Taurus) that allows for the automatic generation of resource and performance compliant ML models which outperform hand-tuned models by up to 16.9% in our tests. Finally, we show how these tools can be assembled to enable an adaptive ML loop in a network. Online labelling of raw data in the network can feed Homunculus, enabling the network to build new ML models from its own packet data. These models can then deploy learned policies in Taurus, setting the groundwork for upcoming AI-driven networks

Keywords: Engineering.
Electrical Engineering.
Advisor: Olukotun Oyekunle Ayinde
Shahbaz Muhammad
Winstein Keith
Towards cost-effective and trustworthy healthcare machine learning

Machine learning (ML) has made exciting progress across many healthcare tasks, such as chest X-Ray interpretation and seizure detection from electroencephalograms (EEGs). However, while ML models exhibit impressive overall performance, we identify two critical roadblocks to model development and deployment: (1) reliance on massive training datasets created via manual expert labeling, resulting in high annotation costs, and (2) reliance on non-generalizable features, resulting in unexpected poor performance on subgroups of patients ("hidden stratification"). As a result, healthcare ML is currently costly to develop and can be untrustworthy to deploy. To address the first roadblock, we develop new forms of cost-effective supervised training of ML models with cross-modal data programming (XMDP). In particular, healthcare data are often accompanied by other data modalities that contain task-related information for which it is feasible to create labeling functions at low cost, such as clinical reports, workflow notes, or (in the future) gaze data (i.e., eye-tracking data). We develop labeling functions that map the auxiliary data modalities (either text or gaze) to labels using only a small amount of manual labels. Once we develop these labeling functions, we are then able to scale labeled training sets without additional annotation costs. To address the second roadblock, we propose to improve robustness of ML models to hidden stratification by increasing task specificity in two exemplar medical tasks, pneumothorax detection in medical imaging and seizure detection in electroencephalogram (EEG) time series data. For medical imaging, as opposed to training a binary classification model for detecting pneumothorax (low task specificity), we first train an image segmentation model to localize pneumothorax (higher task specificity) and use the segmentation output to derive the binary prediction. For detection of seizure on EEG, we increase task specificity by training a model to classify additional attributes in the EEG, such as artifacts. We find that increasing task specificity significantly reduces reliance on non-generalizable features and improves performance among clinically meaningful subgroups, which decreases performance gaps among subgroups and results in more trustworthy models. In summary, our work investigated new forms of model supervision that are less costly than existing approaches, and uncovered strong connections between task specificity and model robustness. While our experiments focus on chest X-ray classification and EEG seizure detection, our proposed methods are applicable to a wider range of healthcare applications.

Keywords: Engineering.
Electrical Engineering.
Advisor: Re Chris
Rubin Daniel
Lee-Messer Christopher
Pauly John
Pilanci Mert
Towards dignity in health : from health-white associations to the reclamation of racial-ethnic minority cultures of health

In 1948, the World Health Organization sought to define health for all peoples beyond the absence of negative symptoms (e.g., illness and disease) to include broad positive aspects (e.g., mental and social well-being), "the enjoyment of which, " it declared, "should be part of the rightful heritage of every human being without distinction of race, religion, political belief, or economic or social condition". Over 75 years later, studies suggest Americans have not adopted this more positive and inclusive definition of health. This dissertation examines the United States' culture of health promotion and how well it resonates with the nation's rapidly growing population of racial-ethnic minority Americans. In this dissertation, I present theory and evidence suggesting that public health promotion in the U.S.--which ought to support the health and well-being of all Americans--privileges some identities and marginalizes others, often the very groups they hope to reach. Using an array of methods, I demonstrate that health and well-being is represented in inequitable ways and suggest how racial-ethnic minority Americans can reclaim a sense of being healthy that feels authentic, affirming, and regenerative of their racial and cultural identity. First, I provide evidence that being healthy is implicitly associated with White people (and being unhealthy with people of color) (Study 1a), an association that extends to health behaviors like eating a nutritious diet (Study 1b). Investigating nutrition promotion more closely, I find that associations between healthy foods and White people also manifest explicitly (Studies 2a-b), and that this conceptualization of health undermines preference for nutritious foods. In light of this problem, I develop and test intervention messaging (Study 3) that more effectively supports racial minorities as they reclaim their healthy food-ways by acknowledging diverse, nourishing culinary traditions beyond those of Euro-centric cultures that currently dominate nutrition promotion. By counteracting stigmatized narratives within healthy eating promotion and offering more affirming and agentic interpretations of health and self, this dissertation presents a critical step towards racial minority Americans reclaiming what it means to eat healthy and be well

Keywords: Humanities and Sciences.
Psychology.
Advisor: Walton Gregory M. (Gregory Mariotti)
Crum Alia
Markus Hazel Rose
Steele Claude
Towards high-performance discontinuous Galerkin simulations of reacting flows using legion

High-fidelity scale-resolving computational fluid dynamics (CFD) simulations may provide a path towards predictive capabilities for many engineering-relevant applications, in particular those involving the interaction between multiple physics such as turbulent multi-component reacting flows. However, their routine use is hindered by their cost. The throughput of the fastest supercomputers is fortunately still increasing but this comes at the cost of specialized devices and hardware heterogeneity. Modern supercomputing thus imposes new requirements onto numerical algorithms and their implementation for leveraging the available compute while significantly complicating the design, development, and maintenance of massively parallel computational physics software. This dissertation focuses on addressing numerical and implementation challenges of CFD of multi-component reacting flows. It considers high-order discontinuous Galerkin (DG) discretization methods as a way towards a more efficient use of modern compute architectures for performing high-fidelity simulations of configurations involving complex geometries. Both the high accuracy requirement and the ability to accommodate unstructured meshes are key motivations for investigating this class of numerical schemes. The entire content of this manuscript is based on DG-Legion, a novel unstructured DG CFD solver for the compressible Navier-Stokes equations written on top of the Legion system for targeting distributed heterogeneous architectures. The main challenge related to the computer science aspect was to scale the code on GPU supercomputers. An explicit ghost formulation is proposed which results in performance competitive with state-of-the-art solvers based on the message-passing interface (MPI) system while the application code remains free of explicit communication and synchronization, i.e. it has sequential semantics. The contribution related to the numerics addresses two important questions. First, the issue of spurious pressure oscillations generated by fully-conservative schemes for thermally perfect mixtures is analyzed in detail. A generic framework is developed in order to generate systematic variations of the baseline scheme with improved properties. When applied to the problem at hand, several techniques that were previously proposed independently are recovered as special cases in a rigorous way. Finally, a hybrid strategy for preserving the positivity of DG solutions for the composition-describing scalars is introduced. The method based on the composition of subcell finite volume schemes and linear-scaling limiters leads to an overall positivity-preserving scheme in the inviscid limit, compatible with higher-order quadrature rules, and better preserving the subcell resolution compared to only using rescaling techniques

Keywords: Engineering.
Aeronautics and Astronautics.
Advisor: Aiken Alexander
Lele Sanjiva K. (Sanjiva Keshava)
Alonso Juan José
Towards longitudinal mapping of transportation infrastructure with spatio-temporal generative modeling

This work proposes a super-resolution pipeline to generate high resolution satellite imagery for a preferred point in time. We use a spatio-temporal generative model to perform super-resolution of low spatial resolution (LR) satellite imagery from the preferred time, conditioning on high resolution imagery (HR) from another time to generate HR satellite imagery for the preferred time. To train the spatio-temporal generative model, we curated the largest-ever super-resolution dataset of satellite imagery at sub-meter resolution, consisting of over 96, 000 image triplets. We present a proof-of-concept of this approach by evaluating the performance of our generated imagery on a sidewalk segmentation task. Our results demonstrate that our pipeline generates high spatial resolution images that are structurally similar to the original high resolution satellite imagery while accurately reproducing the style and content of the input low resolution imagery. Furthermore, our generated imagery outperforms LR imagery in the sidewalk segmentation task, providing compelling evidence for the utility and effectiveness of our approach. Since LR satellite imagery is available longitudinally at large geographical scale, researchers can use our pipeline to generate HR satellite imagery for their preferred time and location. Future work could leverage generated longitudinally available HR satellite imagery to longitudinally map transportation networks to inform and evaluate transportation planning.

Keywords: Super-Resolution
Remote-sensing images
Transportation > Planning
Generative Adversarial Networks (GANs)
Climate change mitigation
Artificial intelligence > Industrial applications
Machine learning > Industrial applications
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Thesis
Advisor: Mitchell John
Ng Andrew