VictorJuiz/thesis huggingface dataset:
Deciphering regulatory DNA with deep learning models and interpretation methods
Keywords: Engineering.

Accurate predictive modeling of gene regulation is crucial for a fundamental understanding of cell identity and function. High-throughput profiling of diverse biochemical and functional properties of cells has enabled powerful deep learning based DNA sequence models that predict protein-DNA binding, chromatin accessibility and histone marks across cell types with state-of-the-art accuracy. Interpretation of these DNA sequence models has revealed novel insights into the cis-regulatory code of TF binding, effects of sequence variation and repeats, and the sequence basis of chromatin accessibility. However, there is much scope for enhancing modeling strategies, model performance, and the tooling and infrastructure around model development and interpretation. Moreover, the full potential of these models for extracting biological insights from high-throughput functional profiling data has not been realized. In this thesis, I will present novel methods that advance DNA sequence models for regulatory genomics, and some applications of these DNA sequence models to glean insights into biological systems. First, I introduce ChromDragoNN, a method that enables generalization of DNA sequence models to make predictions in new cell types. Next, I describe fastISM, an algorithm to significantly speed up variant scoring for convolutional neural networks. I then present dynseq, a tool for sharing and visualization of model-derived importance scores of individual bases. I will then apply DNA sequence models to two different biological systems. First, I combine single-cell chromatin accessibility profiling with DNA sequence models to nominate regulatory DNA variants associated with eye disorders. Next, I apply DNA sequence models to the study of single-cell chromatin accessibility from a time course of human skin cells transforming into induced pluripotent stem cells over four weeks. Using DNA sequence models, I reveal mechanistic insights into reprogramming progression by linking transcription factor abundance changes to sequence logic encoded in regulatory elements. Together, this thesis advances predictive modeling and analysis of gene regulation through new methods, tools and biological applications. I hope that the work moves the field closer to realizing the full potential of DNA sequence models for understanding cell identity and function.
Deep learning based LArTPC event reconstruction and nonmagnetic muon sign determination in the ICARUS detector
Keywords: Humanities and Sciences.
Physics.

The Liquid Argon Time Projection Chamber (LArTPC) is a state-of-the-art particle imaging detector technology pioneered at the kiloton scale by the ICARUS detector, now at Fermilab. It will be widely used in accelerator-based oscillation experiments for precision measurements of neutrino oscillation parameters. This thesis demonstrates a novel end-to-end, deep-learning-based data reconstruction chain for LArTPC. This approach can scale up to large existing and upcoming LArTPC neutrino detectors such as ICARUS and DUNE with sparse convolution techniques. We apply it to an electron neutrino selection on ICARUS simulation, which shows promise for a future low-energy excess search. This work further presents two methods for nonmagnetic muon sign determination in ICARUS: comparing the muon decay electron energy spectrum with bound muon decay-in-orbit numerical predictions, and fitting the muon decay time distribution. ICARUS cosmic ray data analysis yields a muon to antimuon ratio of 0.69 +/- 0.12 in agreement with previous measurements. Such methods for statistical separation between muon and antimuon, when applied to a muon neutrino/antineutrino beam, are expected to improve the sensitivity of ICARUS and forthcoming LArTPC experiments to CP-violation, neutrino mass ordering, and neutrino oscillation measurements
Deep Learning for Seismic Resolution Enhancement, Enhancing Low-Resolution Legacy Data Using High Resolution Guidance
Keywords: Saudi Aramco
Seismic
Deep learning (Machine learning)
Image processing
Text
Thesis

This study investigates the utilization of the CycleGAN neural network architecture to potentially enhance resolution in subsurface seismic imaging. While convolutional neural networks (CNNs) have been extensively applied for image enhancement, prior research mostly focused on increasing pixel resolution for detailed image enhancement. However, this thesis uniquely targets vertical and horizontal resolution issues in seismic data, often leading to undetected geological features largely due to insufficient frequency content in recorded seismic data. Enhancing subsurface seismic images involves significant resource costs, but this thesis showcases the potential to mitigate these costs by employing machine learning to enhance older-generation seismic data. Utilizing 3D seismic data from Saudi Arabia, including both older and higher resolution datasets, the research aims to address the frequency content and processing algorithms differences in historical datasets by training a CycleGAN model with a U-net as the CNN. The model aims to convert legacy seismic images to closely resemble higher-resolution datasets. Initial findings reveal noticeable resolution improvements, yet further refinement opportunities exist.
Deep learning on a diet : an error landscape perspective on parameter and data efficiency in deep learning
Keywords: Humanities and Sciences.
Applied Physics.

Many of the recent remarkable breakthroughs in artificial intelligence have come from scaling up both the number of parameters in artificial neural networks (ANNs) and the size of datasets used to train them. This scaling however is unsustainable and it is important to develop methods to achieve similar results under resource constraints. But when and how can we decrease the number of parameters and examples while still training ANNs to the same performance? We investigate methods for pruning both network parameters and the dataset from the perspective of the optimization error surface for ANNs. Through an empirically driven investigation, we show how geometric properties of the error landscape, such as curvature and error basins, determine how many parameters can be pruned and which examples are important for generalization. Overall, we take a step towards understanding the scientific principles that underlie data and parameter efficiency in ANNs.
Demanding Beauty: A Critical Analysis of Quality of Life for Women with Facial Disfigurement
Keywords: Quality of Life
Facial disfigurement
Facial difference
Plastic &amp; Reconstructive Surgery
Craniofacial Abnormalities
Deformities
Text
Thesis

Facial disfigurement has been found to be associated with poverty, social exclusion, low self-esteem, and an overall diminished quality of life compared to those without facial disfigurement (a.k.a. facial difference). Despite the lack of clear evidence on whether surgical interventions can enhance the quality of life of individuals with facial disfigurement, many medical practitioners still offer such treatments in an effort to improve their patients' well-being. This research sought to examine under what conditions treatment for facial disfigurement does indeed lead to a better quality of life. The study revealed that women who felt they had a sense of agency in choosing to receive treatment were more likely to report a high quality of life. Additionally, these women reported feeling positive about their overall healthcare experience and feeling highly respected by others. This paper concludes by discussing the implications of these findings for gaining useful insight into how resources can be improved to help meet the disfigurement community’s needs.
Design and modeling of variable stiffness and shape-changing robots
Keywords: Engineering.
Mechanical Engineering.

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
Design and optimization of active photonic devices
Keywords: Humanities and Sciences.
Applied Physics.

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.
Design and perception of mobile tactile displays
Keywords: Engineering.
Mechanical Engineering.

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.
Design and validation of an in situ 3D PTV system for ocean turbulence
Keywords: Engineering.

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.
Design of exoskeletons and robotic tools for improving mobility
Keywords: Engineering.
Mechanical Engineering.

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.
Designer quantum materials and devices with spin-like degrees of freedom
Keywords: Humanities and Sciences.
Physics.

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.
Designing and evaluating language models for human interaction
Keywords: Engineering.

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
Designing cell-instructive granular inks for 3D bioprinting
Keywords: Engineering.
Bioengineering.

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
Designing nanoprobes for scalable electrophysiology and curvature sensing
Keywords: Humanities and Sciences.
Chemistry.

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.
Detecting Rare Events Using a One-Class Classifier Based on Diffusion Autoencoders
Keywords: anomalydetection
Text
Thesis

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.