VictorJuiz/thesis huggingface dataset:
Correspondence detection in natural terrain : computer vision techniques for noisy 3D range data

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.

Keywords: Engineering.
Aeronautics and Astronautics.
Advisor: Rock Stephen M.
D'Amico Simone
Schwager Mac
The cyber spiral? : operational and political implications of emerging domains of warfare

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.

Keywords: Humanities and Sciences.
Political Science.
Advisor: Schultz Kenneth A.
Fearon James D.
Sagan Scott Douglas
A decision-making model for retired Li-ion batteries

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.

Keywords: Engineering.
Materials Science and Engineering.
Advisor: Benson Sally
Chueh William
Onori Simona
Decoding retinal signals with denoising natural image priors

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.

Keywords: Engineering.
Electrical Engineering.
Advisor: Chichilnisky E.J.
Mitra Subhasish
Wetzstein Gordon
Deep learning and CRISPR-Cas13D ortholog discovery for optimized RNA targeting

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.

Keywords: Engineering.
Bioengineering.
Advisor: Konermann Silvana
Bintu Lacramioara
Chang Howard Y. (Howard Yuan-Hao)
Quake Stephen Ronald
Deep learning on local sites for protein structure and function analysis

Understanding how the three-dimensional structure of a protein leads to its function is important for determining disease mechanisms, developing targeted therapeutics, and engineering new proteins with desired functional characteristics. The expansion of protein structure databases due to experimental and computational advances provides an unprecedented opportunity to learn structure-function relationships in a data-driven manner. Deep learning methods that operate on protein structures have shown promise for specific tasks, but their utility for functional analysis has been limited due to inconsistencies in model training and evaluation, lack of labeled protein function data, and an inability to reconcile global predictions with local biochemical mechanisms. In this dissertation, I explore these challenges and propose a framework for protein analysis based on learning on local sites rather than the entire protein structure. First, to establish standards for model development and evaluation, I present work on (1) developing a suite of benchmark datasets, processing tools, and baseline models, and (2) quantifying the effect of differing structure compositions in the training data. I then describe a self-supervised learning method that leverages evolutionary relationships to learn general-purpose representations of local structural sites, and show how these representations enable improved performance on downstream tasks involving classification, search, and annotation of functional sites. By clustering millions of sites, I propose a framework for protein analysis based on conserved structural motifs which enables the discovery of functional relationships across protein classes. Finally, I present a method for explainable function annotation that predicts the overall function of a protein as well as the individual residues which are responsible.

Keywords: Medicine.
Biomedical Data Science.
Advisor: Altman Russ
Dror Ron
Huang Possu
Leskovec Jurij
Design technology co-optimization of energy-efficient digital logic using carbon nanotubes

Carbon nanotube (CNT) field-effects transistors (FETs) are promising candidates for high-speed and low-energy digital logic. CNT FETs have been integrated within multiple industrial fabrication facilities at mature technology nodes (e.g., 90/130nm on 200mm silicon wafers). However, understanding CNT energy and delay benefits at extremely scaled nodes (e.g., beyond 2nm) is difficult due to several reasons: 1. CNT FETs have distinct characteristics -- material properties and fabrication techniques -- versus silicon FETs, that directly impact digital logic energy and delay. 2. CNT FET energy efficiency analysis must include CNT FET minimum leakage current. 3. CNT FET digital logic energy and delay exhibit numerous trade-offs, far beyond those for silicon FETs. I address these challenges through new physics-based models using physically meaningful parameters, new CNT FET doping and layout design techniques, and extensive Design Technology Co-Optimization (DTCO). To illustrate new physics-based models, I present the extended scale length theory. It captures the differences between CNT FET and silicon FET electrostatics that are crucial for energy and delay estimation. It is used to quantify CNT FET minimum leakage current within 3× vs. experimentally calibrated Non-Equilibrium Green's Function (NEGF) solvers. In contrast, prior models can underestimate CNFET minimum leakage current by 105×. Understanding CNT FET digital logic energy and delay trade-offs requires extensive DTCO simulations across many design and technology parameters. My new physics-based models enable fast DTCO: over 350,000 simulations in a few days vs. several months using existing Technology CAD simulators. Such extensive DTCO helps derive CNT FET design and technology parameters with up to 7× projected Energy-Delay Product (EDP) benefits vs. silicon FETs at the 2nm technology node. These DTCO simulations also include a new CNT FET extension doping technique based on the concept of a barrier booster. A new logic layout technique called Omni 3D exploits CNFET low-temperature fabrication to further enable up to 1.9× additional projected EDP benefits. Several of my thesis contributions extend beyond CNT FETs, e.g., to FETs based on two-dimensional materials.

Keywords: Engineering.
Electrical Engineering.
Advisor: Mitra Subhasish
Pop Eric
Wong Hon-Sum Philip
Discovery and engineering the TG2/LRP-1 pathway for lysosomal delivery of molecular cargo : implications for celiac disease

Celiac disease (CeD) is a systemic, inflammatory immune disorder that primarily affects the small intestine. It is triggered by the consumption of dietary gluten, derived from wheat, barley, or rye in certain genetically susceptible individuals. Gluten peptides are rendered highly antigenic to CeD patients through deamidation at certain epitopes by transglutaminase-2 (TG2), a widely expressed and multifunctional enzyme. Over 95% of all CeD patients express HLA-DQ2, and a vast majority of the remaining population express HLA-DQ8, and in these individuals the deamidated peptides tightly bind MHC-II on the surface of antigen presenting cells (APCs) and are then presented to gluten-specific CD4+ T cells. TG2 activity is necessary for peptide deamidation as demonstrated by restoration of a healthy phenotype upon TG2 inhibition in a mouse model of CeD. Thus, understanding the activity and localization of TG2 could help understand the mechanisms underlying CeD, and how APCs take up TG2 and gluten peptides leading to the hallmark T cell response. In my dissertation I discuss a new pathway for gluten antigen uptake and presentation in celiac disease, and how this pathway can be utilized for therapeutic applications. This pathway relies on the protein-protein interactions between three different widely expressed proteins, TG2, α2-macroglobulin and LRP-1. The complex that forms between TG2 and its gluten substrate is recognized by α2- macroglobulin, an abundant plasma protein, which leads to a conformational change. This conformation is recognized by its canonical cell surface receptor, LRP-1, triggering an endocytic event eventually delivering the cargo to the lysosome. In HLA-DQ2 expressing cells the deamidated gluten peptide released in the lysosome binds to MHC-II and is presented on the cell surface. The antigens presented on the cell surface through this pathway efficiently activate gluten-reactive T cells. Finally, this pathway can be hijacked to induce the lysosomal degradation of proteins of interest. A ligand to the protein of interest is conjugated to a gluten peptide, allowing for the entire complex to be endocytosed in an LRP-1 dependent manner. The molecular cargo, including the protein of interest is degraded, and LRP-1 is recycled to the cell surface. This approach could be used to target therapeutically relevant proteins without needing to inhibit them, in a manner analogous to LYTACs, but using only small molecules. Overall, this work offers insight into the molecular mechanisms behind antigen presentation in CeD, and how this knowledge can be used to design novel therapeutics for CeD and other diseases.

Keywords: Engineering.
Chemical Engineering.
Advisor: Khosla Chaitan
Abu-Remaileh Monther
Swartz James R.
Discovery of the lysosomal bis(monoacylglycero)phosphate synthase

Lysosomes maintain cellular homeostasis in diverse nutrient environments through the degradation and recycling of biological molecules, such as proteins, water-soluble metabolites, and lipids. As such, lysosome dysfunction underlies monogenic and complex neurodegenerative diseases. Strikingly, bis(monoacylglycero)phosphate (BMP) is a lysosome-resident glycerophospholipid that potently stimulates lysosomal catabolism, and its abundance is aberrant in neurodegeneration. Given its essential role in lysosome function, modulating BMP may ameliorate neurodegeneration; however, the enzyme responsible for its synthesis has been unknown for over a half a century, slowing progress in this field. This dissertation employs interdisciplinary tools to identify and validate the elusive BMP synthase (BMPS) and define its role in monogenic neurodegeneration. Chapter 1 explores cytopathology shared between monogenic and complex neurodegenerative diseases and establishes lysosomal storage disorders as a model for understanding neurodegeneration. Chapter 2 chronicles BMP's discovery in 1967, details both its structure-activity relationship and metabolism, and posits hypotheses for how the modulation of BMP may be therapeutically efficacious in neurodegeneration and other lysosome-associated diseases. Chapter 3 demonstrates that the Batten disease gene product CLN5 is the long-sought lysosomal BMPS and how the loss of BMP inhibits lysosomal lipid metabolism. Chapter 4 reveals that glycerophosphodiesters (GPDs) inhibit BMPS, establishing a metabolic connection between CLN3 and CLN5 Batten disease. Chapter 5 engineers an enzyme-based tool to detect and measure GPDs in diverse biological matrices. Chapter 6 looks ahead to the trajectory of the burgeoning field of BMP in health and disease. This work provides a genetic handle to study BMP and pioneers its therapeutic potential in neurodegeneration and beyond.

Keywords: Medicine.
Biochemistry.
Advisor: Abu-Remaileh Monther
Khosla Chaitan
Long Jonathan Z.
Mochly-Rosen Daria
Pfeffer Suzanne
Dissecting the cellular and molecular basis of photosymbiosis in the acoel convolutriloba longifissura

Multicellular organisms are now commonly regarded as holobionts, collections of species making up an individual. The intimate relationships between symbiotes in a holobiont seem to involve the molecular interdependence of species to varying degrees. Studying the integration of molecular pathways in holobionts has been experimentally challenging, but high-throughput sequencing technologies now allow for the simultaneous evaluation of multiple partners at once. In this dissertation, we use the photosymbiotic acoel Convolutriloba longifissura to evaluate some of the implications of the molecular crosstalk between species. In Chapter 2, we evaluate the responses of algal symbionts to host regeneration. Early post-injury the algal response is defined by a decrease in photosynthetic efficiency and an upregulation of photosynthesis-related genes. A second wave of transcriptional responses takes place two days post-injury, with unique molecular players. We demonstrate that regeneration of the holobiont is dependent on the transcription factor, Cl-runt, a conserved regulator of acoel regeneration. Knockdown of Cl-runt further decreases the operational quantum yield of photosystem II and dampens the early and late transcriptional responses in both the host and the symbionts. This work describes the coordination of molecular responses to regeneration in a photosymbiotic holobiont. In Chapter 3 we characterize the cell type diversity found in C. longifissura and compare their cell type composition to the non-symbiotic acoel Hofstenia miamia. We identify 17 unique cell types belonging to all expected tissue types: epidermal, muscle, neural, endoderm-like, secretory, and stem cells. Based on their relative DNA content and gene expression, four populations are cycling stem cells. Comparison with H. miamia demonstrates high cell type similarity. Muscle and epidermal cell types have the best alignment scores, while stem cells share the highest number of supporting genes. The endoderm-like cell types are identified as homologs but show low similarity. However, we identify five orthologous transcription factors shared between acoel endoderm-like cell types: foxA, ets-2, gsc, hnf4, and elf5. Functional characterization is needed to evaluate if these cell types have shared functions in these acoels. Overall, the high similarity found between acoel cell types supports a limited cell type divergence between acoels.

Keywords: Humanities and Sciences.
Biology.
Advisor: Wang Bo (Researcher in bioengineering)
Bergmann Dominique
Lowe Christopher (Associate professor of biology)
Palumbi Stephen R.