VictorJuiz/thesis huggingface dataset:
Beyond text : applying deep learning to signal data
Keywords: Engineering.

Sequence modeling primitives have been responsible for breakthroughs across domains like natural language processing and genomics. Despite these advances, existing primitives still struggle to model the large class of signal data acquired from physical sensors. This data has unique characteristics that make it challenging to model: signal data resolution affects the training and generalization of models, signal data is sampled at high rates, resulting in dense data with long-range dependencies, and signal data is highly diverse, with application areas including healthcare, video processing, and industrial sensing. All of these properties raise the bar for universal approaches to modeling this data. This thesis develops a new set of approaches for modeling signal data using state space models. First, we introduce a sequence model called S4 that serves as a general building block for modeling signal data. Second, we generalize this modeling layer to multidimensional signals like images and videos, yielding the first state-of-the-art signal model on large-scale benchmarks such as ImageNet. Incorporating S4 into a multiscale architecture makes it possible to model extremely long sequences of audio, including on a previously unsolved task involving unconditional autoregressive generation of raw audio samples. Finally, we demonstrate the widespread applicability of our approach to a variety of signal data, including a real-world application involving impedance sensor data used in the diagnosis of gastroesophageal reflux disease. Taken together, this new set of approaches provides a universal and versatile set of primitives for modeling diverse, multidimensional signals.
Beyond the Collaborator Resistor Binary: Reinterpreting "Autumn Letter"
Keywords: Taiwan
Zhu Dianren
Japanese colonies
Text
Thesis

Literature from the Taiwanese colonial period has been largely seen through the collaborator resistor binary, which assigns value to resistance for furthering the national project.
My purpose is to see beyond the collaborator resistor binary and rediscover the obscured uncertainty and messy sentiments of this period. Zhu Dianren’s “Autumn Letter” is one such work that has been interpreted along these lines. Classical interpretations read “Autumn Letter” as resistance literature, anti-Japanese and pro-Qing. I propose an alternative reading of “Autumn Letter” that situates it in the greater context of the Japanese empire and thus understands it as an I-novel. Doing this allows one to shift the lens of analysis to focus on the self and reveals that Zhu’s main character Mr. Douwen is a satire of an old Qing scholar, and that Zhu does not support the Qing as a viable cultural model. However, Zhu’s support for Marxist ideology means that he cannot support the Japanese either. This leaves Taiwan with an uncertain future, and Mr. Douwen with carrying the burden of the struggle of his nation, which is not a teleological national goal but a quest for identity when trapped between two imperial powers.
Capillary-driven boiling and adsorptive water harvesting : enhancing water and energy efficiency in data center cooling
Keywords: Engineering.
Mechanical Engineering.

The dissertation explores advanced cooling technologies for data centers, focusing on capillary-driven two-phase micro-coolers and kinetically enhanced adsorptive water harvesters to address both heat and water management challenges. The study delves into the transport behaviors of capillary-driven boiling and the development of micro-coolers with three-dimensional manifolds, aiming for scalability and energy efficiency. It introduces random-packed copper inverse opals (CIO) as effective wick materials that enhance cooling by optimizing the critical heat flux and thermal resistance. Through semi-analytical modeling, experimental setups like the suspended micro-bridge, and computation fluid dynamics (CFS), the research analyzes steady-state boiling, critical heat flux, and the thermal transients of capillary-driven systems. Additionally, the thesis details the creation of large-area, chip-scale evaporative micro-coolers using CIO films, demonstrating their ability to handle high heat fluxes efficiently while minimizing energy consumption. The interaction between wick structures, manifold designs, and operational conditions such as flow rates is examined to optimize micro-cooler performance. The study also investigates the integration of fluidized metal-organic frameworks (MOF-303) for water harvesting using waste heat from data centers, enhancing the sustainability of cooling processes. This approach not only conserves water but also leverages thermal outputs for additional environmental benefits, illustrating the potential of combined energy and water management strategies in improving data center sustainability.
Chemotaxis to microbial and plant-derived chemicals by C. elegans nematodes and its dependence on feeding state
Keywords: Medicine.

Throughout history, humans have relied on plants for medication, flavoring, and food, and these plants have also coexisted in ecological communities with other plants, fungi, and animals for millions of years, evolving mechanisms of chemical communication. They release wide arrays of compounds into the environment, affecting animal and microbe behavior. Nematodes, like Caenorhabditis elegans, living in such communities must distinguish harmful from beneficial molecules. In this thesis, I present first a new platform we used to determine the chemotaxis valence of single molecules in C. elegans. In a screen of 90 plant molecules, 37 affected wild-type animals. Analysis of mutants defective in chemosensory ion channels reveals that most compounds relied on multiple ion channels, and chemotaxis valence is more likely to reflect an integration of neural signals than a labeled line. Using this platform, I further examined whether the biosynthetic precursors of natural products could also elicit responses, possibly as a co-evolutionary remnant. Many organisms, including plants and fungi, synthesize the strong attractants isoamyl alcohol and 2-methyl-1-butanol, and, when well-fed, wild-type and mutant nematodes exhibited weak or absent responses to precursor molecules, suggesting that they could be detected but were not particularly motivating. Following prolonged starvation, chemotactic responses were altered in two primary ways: responsiveness decreased to both strong attractants, and a new sensitivity to a precursor compound reliant on OSM-9 TRP channel signaling was revealed. These data also provided additional support for neural signal integration of chemosensory cues. Future studies will be needed to understand how starvation induces these changes in chemosensory behavior and signal integration. Understanding these dynamics sheds light on complex community interactions.
City of skulls : art, ritual, and the afterlife in early modern Naples
Keywords: Humanities and Sciences.
Art and Art History.

"City of Skulls" explores the visual culture of death in early modern Naples, examining art, architecture, devotional texts, and ritual objects to reconstruct what anthropologists and social geographers call a "deathscape." During the period considered, Naples endured volcanic eruptions, earthquakes, popular revolt, and plague. As the city's crypts filled with corpses, its inhabitants were bombarded with evidence of human frailty. The seventeenth century is especially dense in morbid art for a study of Neapolitan death culture. This study is composed as three sets of two chapters. The first pair considers the death of an individual, introducing the notion of "the good death" (ben morire) and examining the translation of ben morire devotional practices to a sculptural format. The next pair exposes a surprising overlap between the fields of religious history and environmental humanities, together arguing that the omnipresence of volcanic activity intensified religious imagination of the underworld. The final two chapters consider the burial practices that took hold in an increasingly urbanized Naples, when construction of new buildings and roads supplanted the traditional courtyard cemetery. The Neapolitans found their solution in the subsoil, and there they made space for the practical and spiritual matters of death—that is, burial as well as commemoration through art and ritual.
Computational tools for structure-guided drug discovery
Keywords: Humanities and Sciences.
Chemistry.

Most drugs function by binding to proteins and modulating their function. Computational methods that model the atomic interactions between drug molecules and proteins promise to greatly accelerate drug discovery. This work discusses the application and development of several computational methods, involving molecular simulations and machine learning, for understanding and designing novel therapeutics. First, we apply atomic-level simulations to investigate mechanisms that contribute to drug selectivity and avoid undesired side-effects. We reveal the structural basis for the selectivity of a long-studied Schizophrenia drug between structurally similar muscarinic acetylcholine receptors. We validate this mechanism experimentally through mutagenesis and use it to design molecules with altered selectivity profiles. Second, we develop a series of methods that use machine learning to assist in structure-guided drug design. Using the three-dimensional structure of a target protein, these methods aim to generate novel and diverse drug-like molecules that bind to the target. We compare several approaches, including a fragment-based approach specifically designed to incorporate chemical intuition. Third, we investigate new molecular mechanisms by which drug molecules can modulate the signaling of G-protein coupled receptors. Using atomic-level simulations, we found that certain ligands can trigger activation of free fatty acid receptor 1 by directly rearranging an intracellular loop that interacts with G-proteins. We further supported this non-canonical mechanism through targeted mutagenesis; specific mutations which disrupt interactions with the intracellular loop convert these agonists into inverse agonists in in vitro experiments. This work highlights how computational tools, combined with complementary experimental methods, can accelerating drug discovery by elucidating molecular mechanisms as well as directly facilitating structure-guided drug design.
Convex shape optimization of aerospace vehicles
Keywords: Engineering.
Aeronautics and Astronautics.

The field of aerodynamic shape optimization (ASO) has seen dramatic advances in the last several decades through major developments in computational power and numerical methods. Today, the process of gradient-based optimization of complex, three-dimensional aerospace vehicles, using high-fidelity physical models, is mature. This dissertation explores a new horizon of shape optimization for aerospace vehicles, employing techniques from the field of convex optimization. Unlike traditional nonconvex, gradient-based optimization techniques, which iteratively refine an initial design towards a new local optimum, convex optimization seeks a globally optimal solution. These techniques address two important open problems in ASO. The first is the need to be able to efficiently and robustly explore high dimensional, constrained, multi-objective design spaces in order to assess performance tradeoffs and limits during the initial development of new vehicles. The second problem is linking these exploratory, or conceptual design, studies to the high-fidelity, gradient-based optimization frameworks for final refinement. These frameworks require a parameterization and an initial design point. Good parameterizations and initial design points can significantly decrease the overhead of these computationally expensive processes. Convex optimization offers an exciting avenue to addressing both of these problems. In the first part of this dissertation, I develop a framework called Convexity Assisted Shape Optimization, or CASO. CASO provides a set of rules and requirements for approaching aerospace vehicle shape optimization problems through the lens of convex optimization. I also propose two new types of smooth and accurate convex surrogates that will be useful in reducing this framework to practice. In the second part of this dissertation, I propose several new classes of orthogonal basis functions for parameterizing shapes in aerodynamic shape optimization problems. In some cases, these bases simplify the derivation and expression of useful aerodynamic objective functions. In other cases they offer a natural path to representing important aspects of aerodynamic shapes. I also show how these bases can be used to develop convex formulations of several common aerodynamic performance indicators, spanning multiple flow regimes. In the third part, I extend these methods to nonconvex objective functions that have convex trust regions that may be represented accurately and smoothly using convex surrogates. I also consider the cases of nonconvex objective functions that benefit from a transformation and relaxation strategy or a bi-level optimization scheme to preserve the ability to identify a global optimum. Finally, I show how these methods can be applied to actual design problems and link these conceptual results to a high-fidelity design framework. These design problems span multiple flight regimes, performance indicators, and shape representations, in order to provide a broad sampling of the types of problems that can be approached using CASO.
Correspondence detection in natural terrain : computer vision techniques for noisy 3D range data
Keywords: Engineering.
Aeronautics and Astronautics.

Exploration of small bodies such as asteroids and comets can provide valuable scientific understanding of the history and evolution of our solar system, and a key component in these missions is generating a shape model of the target body. LiDAR systems are an emerging option for small body mapping, but face challenges with the point cloud correspondence detection step of the pipeline. Correlation-based correspondence detection techniques require a good initial alignment in order to reliably converge to an accurate result, and the high uncertainty of small bodies' motion models, coupled with the lack of external navigational infrastructure such as GNSS or fixed beacons makes this a challenging problem. With varying range sensor resolutions, high noise levels, and low-gradient terrain, the signal to noise level of the point cloud can cause feature-based techniques to have a low feature recall and a high ratio of outliers. When the overall number of features is small, which can be common in natural terrain applications and as is expected near safe rover landing sites, state of the art feature-based correspondence methods can unknowingly generate misaligned results. In this thesis, a novel point cloud correspondence detection algorithm is presented that combines constellations of a small number of features within point cloud data with Iterative Closest Point (ICP) alignment solutions. First, a new handcrafted 3D point cloud descriptor, Smooth Signature of Histograms of OrienTations (SmoothSHOT), is tailored to natural terrain. Next, the Pyramid star tracker constellation detection algorithm is fused with the MinBucket graph matching algorithm to provide an efficient database search procedure, Pyramid Edge-Reduction Schema for Efficient Isomorphism Detection (PERSEID). Constellations of features are combined into metafeatures in order to identify the most distinctive constellation matches within the dataset and prevent susceptibility to repetitive or ambiguous geometry. Finally, ICP is initialized with the metafeature alignment, and then the result is validated by it as well. When applied to a model of the asteroid Itokawa with realistic noise and resolution changes, ICP alone achieves a precision of only 60-70% across a range of noise conditions and resolution changes. RANdom SAmple and Consensus (RANSAC) initialized ICP has 99% precision at low noise settings, but only 44% at the realistic noise conditions. The algorithm presented in this thesis, PERSEID-Augmented ICP (PAI), is able to achieve a correspondence detection precision of 100% under the same conditions. On an underwater sonar dataset where ICP and RANSAC-initialized ICP have 53% and 32% precision, respectively, the PAI algorithm is able to achieve 90% precision. Higher-precision correspondence detection results with fewer required feature matches can improve point cloud correspondence detection for extraterrestrial mapping in feature-poor areas.
The cyber spiral? : operational and political implications of emerging domains of warfare
Keywords: Humanities and Sciences.
Political Science.

This project examines how international actors send, receive, interpret, attribute, predict, and act on information about their opponents during security crises, with special consideration to conflict involving emerging domains such as cyberspace. The first paper baselines how states perceive each other's images to determine if there are patterns of perception and misperception in these interpretations. This paper uses content analysis of historical wargames from the Cold War to evaluate how states convey and interpret images of resolve. It finds that strategic misperception is very common, and that actors are more likely to underestimate opponents' images of resolve than overestimate them. It also finds little evidence that significant game moves, often described as signals, effectively shift prior assumptions or beliefs; this suggests that assumptions, biases, and other priors are likely drivers of misperception. The second and third papers use scenario-based survey experiments of elite practitioners to establish how decision-makers consider traditional versus emerging technological domains to (1) interpret the intent of adversary actions and (2) assess and assume risk under conditions of uncertainty. These two papers find that practitioners view military actions in cyberspace with many of the characteristics the literature typically ascribes to signals, and that these practitioners view cyber activity as decidedly less escalatory than kinetic activity. We also find that technological uncertainty has almost no effect on the calculus of decision-makers, but assessments of escalatory risk are highly important. Further, almost all US military respondents coalesce in roughly equal numbers around three distinct decision constructs with distinct logics. The surveys reveal a great chasm between US military and US public decision-making involving emerging technologies. The dissertation contributes substantively to the literature on escalation, risk and uncertainty, misperception, signaling, and conflict in cyberspace.
A decision-making model for retired Li-ion batteries
Keywords: Engineering.
Materials Science and Engineering.

This thesis focuses on the development of a decision-making model to determine the solution for a retired battery. The model evaluates the retired battery from both technical and economical perspectives. It integrates a module-level aging model with an economic evaluation model to assess the values of Second Life Batteries for the most commonly cited second-life use cases. Finally, the highest value among different second life applications is compared with the recycling value to determine the optimal strategy for a given retired Li-ion battery. The initial segment of the thesis is dedicated to crafting a module-level degradation model, followed by the integration of an economic model in the subsequent section to project the resale price of used batteries. The thesis reaches its zenith in the third segment with an illuminating use case study, exemplifying the practical application of the decision-making model. Generally, prioritizing reuse over recycling is economically advantageous. However, specific scenarios may render reuse unprofitable, favoring recycling as a more viable option. This dynamic decision-making model adapts to real-world variables, generating varied optimal strategies in response to evolving conditions.
Decoding retinal signals with denoising natural image priors
Keywords: Engineering.
Electrical Engineering.

The retina transforms and compresses visual information as it encodes incident patterns of light into the spike trains of retinal ganglion cells. Understanding the nature of these signals and the cells that carry them is fundamental both to understanding the visual system, and to the development of retinal prosthetic devices that restore vision. This thesis first explores the content and meaning of the retinal code, using a novel Bayesian maximum a posteriori method for reconstructing (decoding) natural images from the recorded spike trains of large populations of retinal ganglion cells. This method achieves state-of-the-art performance for reconstructing statically-presented natural images, and generalizes straightforwardly to reconstructing natural movies with emulated fixational drift eye movements, while providing an interpretable framework for understanding retinal coding. Application of the method to reconstructing natural movies demonstrates that fixational drift eye movements improve the fidelity of the retinal signal, even if the eye movements are unknown a priori and must inferred from the spike trains. Spike timing precision is found to be particularly important in the presence of eye movements, and stimulus-induced correlated firing between nearby cells is shown to contribute significantly to the content of the retinal code. Separately, this thesis develops a novel optimization-based technique to decompose the extracellularly-recorded spiking waveforms of retinal ganglion cells into distinct contributions from the somatic, dendritic, and axonal cellular compartments. This simple, biophysically-motivated representation effectively extracts physiological properties of retinal ganglion cells from their electrically-recorded waveforms, and correlates strongly with the morphology, receptive field location and structure, and functional cell type of retinal ganglion cells. This technique enables substantial advances in inferring the receptive field locations and the functional cell types of retinal ganglion cells from recorded spiking waveforms alone, addressing challenges in the calibration and operation of an epi-retinal prosthetic device.
Deep learning and CRISPR-Cas13D ortholog discovery for optimized RNA targeting
Keywords: Engineering.
Bioengineering.

Effective mammalian transcriptome engineering technologies are needed to accelerate biological discovery and RNA therapeutics. Despite the promise of programmable CRISPR-Cas13 ribonucleases, their utility has been hampered by an incomplete understanding of guide RNA design rules and computational models for prediction of high efficiency guides. Here, we quantified the performance of 127,000 RfxCas13d (CasRx) guide RNAs and systematically evaluated seven machine learning models to build a guide efficiency prediction algorithm. I further validated the model across multiple genes and human cell types. Deep learning model interpretation revealed preferred sequence motifs and secondary features for highly efficient guides, elucidating CasRx targeting preferences and mechanisms.