VictorJuiz/thesis huggingface dataset:
Design and modeling of variable stiffness and shape-changing robots

Traditional rigid-body robots possess fixed morphology and are created for specific functions. This prevents them from adapting to changes in their environment and limits them to carefully controlled man-made settings. Meanwhile, soft robots face their own distinct challenges due to their infinite number of passive degrees of freedom but few controllable degrees of actuation. Robots that adapt their mechanical properties could address these challenges and better assist humans in unstructured environments. This thesis explores the design, modeling, and fabrication of variable stiffness and shape-changing robots. More specifically, it investigates how variable stiffness can be achieved in both soft and rigid systems and then how it can be leveraged to improve performance and enable new capabilities, such as shape change. At the actuator level, I present a multifunctional soft artificial muscle with variable stiffness and damping that can behave like an actuator, brake, or clutch in a simple, compact form factor. At the system level, I leverage variable stiffness to create robots with properties of both soft continuum and rigid serial chain manipulators by enabling inflated-beam robots with dynamically reconfigurable joints. I also demonstrate a simple method for shape change in everting inflated-beam robots by passively applying strain limiting layers. Finally, I present a lightweight, high extension manipulator which uses variable stiffness to enable shape change. Together, these results in design, modeling, and fabrication address key challenges in using variable stiffness to enable new kinds of actuators and robots for human-robot interaction, manipulation, and exploration

Keywords: Engineering.
Mechanical Engineering.
Advisor: Okamura Allison
Cutkosky Mark R.
Follmer Sean
Design and optimization of active photonic devices

As light passes through a dielectric structure, its properties, such as phase, amplitude, and propagation direction, are determined by the material properties and the structure's design. Active photonic devices, characterized by their tunable properties, such as refractive index, unlock groundbreaking opportunities in various fields, including information processing, optical communication, and exotic physics phenomena. This thesis focuses on exploring the design, optimization, and theory of optical neural networks and nonreciprocal devices by harnessing the tunability of active photonic components. The first part of the thesis delves into the design of a coherent optical neural network utilizing microring resonators for optical computing. The proposed architecture consists of tunable microring resonators as components for linear matrix-multiplication layers and re- configurable nonlinear activation functions. It offers advantages in both device footprint and energy efficiency. We discuss the algorithms developed to train direct tunable parameters in the network using the transfer matrix method and automatic differentiation. The second part of the thesis focuses on nonreciprocal devices based on dynamic modu- lation. We discuss the design for nonreciprocal polarization rotation without using magneto-optical materials, the optimization algorithms using adjoint methods, and a novel modal circulator that achieves amplitude nonreciprocity while preserving mirror symmetry.

Keywords: Humanities and Sciences.
Applied Physics.
Advisor: Fan Shanhui
Brongersma Mark
Safavi-Naeini Amir
Design and perception of mobile tactile displays

We use haptic information to understand and interact with our surrounding environment. Haptic devices can similarly augment our understanding of both our world and digital environments by providing touch-based information and feedback. This thesis examines the design and perception of mobile tactile displays to inform the design of haptic devices that can be used outside of the lab in daily life. First, we present the design and evaluation of a 3-DoF wearable haptic device that can provide normal, torsion, shear, and vibration cues on the forearm. User studies validated that participants could perceive linear shear cues created by the device and that different actuation patterns impact cue identification accuracy. Second, we introduce and test a smartphone platform that can be used to quantify vibration thresholds. We find that the ability to perceive vibrations diminishes in the presence of both physical and cognitive activity and that cognitive activity increases response times to vibrations. Last, we develop a smartphone app that measures response times to multi-modal stimuli. We conduct a user study to measure response times in each condition and find that participants respond faster with higher levels and more types of stimuli while also having a preference toward stimuli that have slower response times. This thesis focuses on understanding the design and perception of mobile tactile displays and provides key insights for future haptic device design. The findings in this thesis can be used to design new devices that deliver touch-based feedback for human-computer interaction.

Keywords: Engineering.
Mechanical Engineering.
Advisor: Okamura Allison
Cutkosky Mark
Salisbury J
Design and validation of an in situ 3D PTV system for ocean turbulence

Small-scale mixing in the ocean has large scale implications for the global ocean circulation, heat budget, and climate system. Physically based parameterizations are necessary to represent these unresolved processes in global ocean models. Efforts to characterize stratified turbulence have been limited by the ability to measure the full time-resolved, small-scale, 3D velocity field. Existing instrumentation, such as shear probes and acoustic doppler current meters, measure velocities or velocity gradients at a single point or in a profile, and thus require invoking assumptions such as isotropy and Taylor's frozen turbulence to estimates of dissipation. Here, we develop a novel instrument to perform in situ 3D particle tracking velocimetry (PTV) to measure the full 3D velocity field, so that quantities like dissipation can be estimated with minimal assumptions. First, this dissertation discusses the conceptual design of the 3D PTV system, specifically the measurement criteria for ocean turbulence and the considerations required to configure a traditionally laboratory technique into a field deployable instrument. Next, the hardware build of the 3D PTV system is detailed, as well as initial validation. Laboratory and field validation was conducted, and demonstrated that the 3D PTV system can measure mean flows, waves, turbulent fluctuations based on comparison with an ADV. The 3D PTV system was able to measure particle tracks within a 20cm^3 volume, and second order Eulerian structure functions could be calculated. The structure function followed the r^(2/3) inertial range scaling between 100-160mm, and this result was consistent for different sequences and different averaging intervals. The findings validate a proof-of-concept for the use of 3D PTV for studying ocean mixing.

Keywords: Engineering.
Advisor: Ouellette Nicholas (Nicholas Testroet)
Koseff Jeffrey Russell
Monismith Stephen Gene
Design of exoskeletons and robotic tools for improving mobility

Regular engagement in physical activity has been strongly correlated to a wide range of health benefits, such as improvements in cardiovascular health, mental health, and decreases in risks of contracting a wide range of chronic diseases. However, some people face barriers that make it challenging for them to remain physically active. Older adults, amputees and other populations that suffer from high fall risk tend to have much lower activity levels due to their fear of falling. Activity levels of young adults have also been decreasing over the past decades, but there are thankfully studies that show that reducing physical and social barriers to exercise can help encourage greater levels of participation in physical activity. In my doctoral research, I designed two devices that can contribute towards enabling higher levels of physical activity. I designed an open-source device that renders bumps and force-fields at a person's pelvis to help researchers study human balance and fall prevention strategies in the lab. Such a device can help accelerate the development of devices or training protocols that might help reduce fall rates and encourage a more active lifestyle in groups with high fall risk. I also designed a portable, powered ankle exoskeleton that provides running assistance, allowing users to reduce the physical barriers of entry to running, and increase peer motivation by enabling friends of different running abilities to exercise together.

Keywords: Engineering.
Mechanical Engineering.
Advisor: Collins Steve (Steven Hartley)
Cutkosky Mark R.
Okamura Allison
Designer quantum materials and devices with spin-like degrees of freedom

Atomic manipulation in a low-temperature ultra-high vacuum scanning tunneling microscope (STM) enables us to construct and characterize a wide variety of two-dimensional systems. We utilize this technique to create materials and devices with unique electronic behavior that would be difficult or impossible to observe in naturally-occurring systems. This thesis serves as a summary of all such systems that I have studied, with special emphasis placed on the two systems which represent the bulk of my work: quantum switches and molecular graphene. These two systems and the studies performed on them differ significantly, with the most stark contrast being the spatial extent of the systems concerned. However, they are linked by the presence of a 'spin-like' degree of freedom that influences the behavior of the system. After an introduction discussing the physics behind STM and atomic manipulation in general, this thesis will discuss each of these systems in turn, including both the experiments performed on these systems and potential for future work.

Keywords: Humanities and Sciences.
Physics.
Advisor: Manoharan Hari
Feldman Ben
Gratta Giorgio
Designing and evaluating language models for human interaction

Despite the ubiquity of language models (LMs) in real-world applications (e.g., web search, text autocomplete, and content generation), most LMs are not optimized for, nor are they evaluated on, real-world usage where human users interact with LMs. To address this gap, this dissertation focuses on designing and evaluating LMs for human interaction. We first start by focusing on one specific need that writers encounter in the revision process: coming up with content given surrounding context. To support this need, we propose a training method to enable any pre-trained LMs to accomplish the task of filling in the blanks, helping to better facilitate human-LM interaction. Second, we build a platform, CoAuthor, to capture human-LM interaction as interaction traces. With CoAuthor, we demonstrate how collecting a large interaction dataset and analyzing the traces provide unique insights into LM capabilities regarding language, ideation, and collaboration in human-LM interaction. Lastly, we propose a new evaluation framework, Human-AI Language-based Interaction Evaluation (HALIE), that defines the components of interactive systems and metrics for human-LM interaction tasks beyond writing. Finally, we discuss open challenges and future directions in this field

Keywords: Engineering.
Advisor: Liang Percy
Bernstein Michael S.
Yang Qian
Designing cell-instructive granular inks for 3D bioprinting

Hydrogel-based biomaterials have seen widespread use in tissue engineering and 3D bioprinting due to their extensive tunability, which enables the design of matrices with a broad range of application-specific mechanical and biological characteristics. Despite this, the viscoelastic requirements of 3D bioprinting make it difficult to design hydrogel-based biomaterial inks that maintain printability while effectively promoting cell function. Granular hydrogels composed of jammed hydrogel microparticles (termed 'microgels') have recently garnered significant attention for their potential to solve this challenge. As cell culture platforms, granular hydrogels may readily provide microenvironmental signals proven to regulate cell behavior. Meanwhile, the macroscale properties of granular hydrogels naturally tend to meet the viscoelastic requirements of extrusion-based bioprinting. In this thesis, I explore strategies for addressing current limitations in designing biomaterial inks to influence endothelial cell behavior within printed constructs. To that end, I present two families of multi-component granular inks that leverage microgel modularity to achieve desirable printability, microarchitecture, and cell response. Through this work, I demonstrate the potential for modular, microgel-based inks to address longstanding challenges in the bioprinting of functional tissues

Keywords: Engineering.
Bioengineering.
Advisor: Heilshorn Sarah
Skylar-Scott Mark 
Yang Fan
Designing nanoprobes for scalable electrophysiology and curvature sensing

The advancement of nanotechnology-driven platforms has introduced numerous promis- ing biological applications in cells. Vertical nanoprobes, capable of detecting molecules and bioactive signals without labels in real-time and with high sensitivity, have become valuable tools in electrophysiology and mechanobiology. This thesis explores the diverse applications of vertical nanoprobes. The first part of this thesis focuses on nanoelectrode arrays, particularly their role in ad- dressing a significant challenge in pharmaceutical development: drug-induced cardiotoxicity. We introduce vertically-aligned nanocrown electrodes, an innovative technology with a high success rate for recording intracellular action potentials (iAPs). This breakthrough enables reliable assessment of drug-induced arrhythmogenicity. Using nanocrown electrode arrays, we can conduct precise parallel iAP recordings, facilitating comprehensive comparisons of cellular responses before and after exposure to pharmaceutical agents, all within a single cell. The latter part of our exploration delves into mechanobiology, where we demonstrate the applicability of the NanoCurvS platform for quantitatively analyzing curvature-sensitive proteins. This platform sheds light on how these proteins behave when faced with the challenges of low curvature, providing valuable insights into membrane curvature biology. These advancements represent significant progress in biomedical research, offering the potential for safer drug development and a deeper understanding of complex cellular processes. The integration of nanotechnology into electrophysiology and mechanobiology opens up new opportunities for innovative solutions and discoveries.

Keywords: Humanities and Sciences.
Chemistry.
Advisor: Cui Bianxiao
Moerner W. E. (William Esco)
Wu Joseph Ching-Ming
Detecting Rare Events Using a One-Class Classifier Based on Diffusion Autoencoders

Existing video anomaly detection methods frequently use image models, extrapolated to frame-level, to detect anomalies in video data. These methods have shown reasonable success on standard benchmark datasets; however, such techniques largely rely on hand-crafted features or pre-determined assumptions about the dataset. For example, they may limit anomalies to specific types, such as person-centric or object-centric, or presume that frame-level information can capture the anomalies present in the dataset. In this paper, we introduce an approach using Diffusion Autoencoders (DiffAEs) for video anomaly detection.

Keywords: anomalydetection
Text
Thesis
Developing methods for simulating reactive condensed-phase chemical systems via quantum dynamics and machine learning

This thesis explores a variety of algorithmic advances that enable the ab initio simulation of condensed-phase chemical systems at reduced computational cost and at the time and length scales required to statistically converge physical properties. We first employ multiple time scale techniques to obtain nanosecond-scale ab initio molecular dynamics simulations of excess protons in two hydrogen-bonded liquids, imidazole and 1,2,3-triazole, using density functional theory (DFT) at the generalized gradient approximation (GGA) level of accuracy. These liquids transport protons via a structural mechanism similar to that of water, and their chemical motifs are present in systems ranging from biological proton channels to proton exchange membrane fuel cells. Using a time correlation function analysis, we decompose the proton transport mechanism into three first-order processes with distinct characteristic timescales, thereby providing a fuller picture of the associated structural transport mechanism. We also uncover two mechanisms that slow down the rate of proton transfer in 1,2,3-triazole relative to imidazole and thus explain their experimentally observed 10-fold difference in proton conductivity. We then develop machine-learned potentials (MLPs) capable of modeling excess protons and hydroxide ions in liquid water at the GGA and hybrid DFT levels of accuracy and use them to perform multi-nanosecond classical and path integral simulations of proton defects in water at a fraction of the cost of the corresponding ab initio simulations. The transport of proton defects in water is of particular interest because it underlies several important chemical and biological processes. Our results show that the MLPs are able to reproduce ab initio trends and converge properties such as the diffusion coefficients of excess protons and hydroxide ions. We use the multi-nanosecond MLP trajectories to study with unprecedented statistical accuracy the role of hypercoordination in the transport mechanism of the hydroxide ion and provide further evidence for the asymmetry in diffusion between excess protons and hydroxide ions. Finally, we develop a computationally efficient quantum-classical Ehrenfest approach based on pure quantum states for the accurate simulation of linear and nonlinear electronic spectra. We demonstrate the performance of our method by showing that it is able to quantitatively capture the exact linear, 2D electronic spectroscopy, and pump--probe spectra for a Frenkel exciton model in slow bath regimes and is even able to reproduce the main spectral features in fast bath regimes.

Keywords: Humanities and Sciences.
Chemistry.
Advisor: Markland Thomas E.
Martinez Todd J. (Todd Joseph)
Rotskoff Grant
The development and analysis of United States and India wind energy atlases that account for multiple altitudes, wind speed thresholds, and exclusions

Climate change, air pollution, and energy security are significant challenges that this dissertation aims to help address. It does so by quantifying, for the first time, onshore and offshore wind resources at different relevant altitudes over both the United States and India, two of the largest global carbon emitters. Today, the United States is far short of meeting climate targets despite a rising penetration of onshore wind energy and a rapidly mobilizing offshore wind industry. India faces the challenges of meeting rising energy demand from a large and growing population, providing reliable and fully electrified energy, and eliminating its pollution and carbon emissions. Here, wind energy atlases were developed for the United States and India using Geographic Information System (GIS) mapping that accounts for technical, climate, environmental, and social exclusions. These exclusions delineate areas where wind farm development is not permitted due to land or marine conservation, infrastructure and safety requirements, existing use by other stakeholders, and climate and ocean restrictions such as wind speed and water depth thresholds. Informed by policy and regulation, setback distances are applied from possible turbine locations to fully account for siting constraints. The outcome is a series of maps illustrating the best places to develop wind farms. Wind energy potential is quantified for multiple hub heights between 100-250 m, with several wind speed threshold, wake loss, and turbine scenarios. These maps will increase certainty for resource planning, significantly expedite the wind farm siting process, reduce investment risk, decrease future project costs, and increase access to key data for energy planners. Findings indicate that, with all but the highest wind speed thresholds, more than sufficient area and potential are available to meet U.S. and India demand for all energy purposes by 2050 using wind energy alone. With economically viable wind speed thresholds and hub heights of modern wind turbines, ~27% of onshore and ~64% of offshore U.S. areas are available, while ~23% of onshore areas in India are available. This translates to 32 TW, 27 TW, and 6.5 TW of potential, respectively. Wind power availability is generally more sensitive to increasing wind speed thresholds than hub height. The U.S. offshore analysis presents an additional economic heatmap to indicate low-cost areas and a map of optimal turbine foundation type. The India onshore analysis quantifies maximum area and potential for repowering, as well as the co-locational opportunities for wind with solar PV resources. Atlases with this detail and scope will help bring wind energy ambitions to fruition, driving the U.S. and India closer to a clean, reliable, and equitable renewable energy system.

Keywords: Enevoldsen, Peter, degree committee member.
Engineering.
Advisor: Jacobson Mark Z. (Mark Zachary)
Gorle Catherine
Development and deployment of machine learning in medicine

Recent advances in machine learning have enabled important applications in medicine, where many critical tasks are tedious and time-consuming for clinicians to perform. This dissertation presents work on using machine learning for cardiology, pathology, and RNA sequencing. This dissertation begins with several applications of machine learning in cardiology, focusing on echocardiograms, or ultrasounds of the heart. Conventional assessment of echocardiograms requires tedious annotation by a human expert. First, I introduce EchoNet-Dynamic, an algorithm for assessing cardiac function from echocardiograms. EchoNet-Dynamic is then integrated into a clinical system and evaluated with a blinded randomized clinical trial. Extensions of the algorithm to pediatric patients and emergency department point-of-care echocardiograms are then presented. This dissertation then presents work applying machine learning to pathology and RNA sequencing. First, I present in silico-IHC, which predicts immunohistochemical stains from commonly available histochemically-stained tissue samples. Next, I present ST-Net, which combines RNA sequencing and pathology by estimating spatial transcriptomics measurements from microscopy images. Finally, I present CloudPred, which predicts patient phenotypes from single-cell RNA sequencing data

Keywords: Engineering.
Advisor: Ermon Stefano
Zou James
Kundaje Anshul