VictorJuiz/thesis huggingface dataset:
Control-oriented learning for robotics and dynamical systems
Keywords: Engineering.
Aeronautics and Astronautics.

Data-driven learning for robotics is ultimately done in the interest of informing decision-making and control, particularly in unseen and unstructured environments. This thesis is centered on the concept of control-oriented learning, whereby learning is attuned to this desired goal, rather than to naively fit models to data. To this end, this thesis presents novel methodologies that tailor model learning towards closed-loop feedback control for robotic systems. In Part I of this thesis, we focus on the offline learning setting. We leverage nonlinear control theory to regularize data-driven model fitting optimization problems to be better aligned with the task of trajectory tracking. In Chapter 2, we propose to do this by adding constraints to the learning problem that encourage the learned model to be stabilizable by jointly learning a dynamics model and a control-theoretic certificate which guarantees the existence of robust tracking controllers for arbitrary open-loop trajectories generated with the learned model. In Chapter 3, we take a different approach by embedding intrinsic factorized structure into our learned dynamics model that engenders the immediate synthesis of a performant tracking controller. Whether through constraints or model structure, we demonstrate that imbuing the offline learning problem with control-theoretic notions results in a model that performs better in closed-loop control online. In Part II of this thesis, we shift our focus to the online learning setting through the lens of adaptive control. In Chapter 4, we propose to learn a feature basis for adapting dynamics models offline through differentiable closed-loop simulation of classical adaptive control laws with parametric dynamics models. Ultimately, this takes the form of a meta-learning framework that links gradient descent on a trajectory tracking performance metric with a parametric representation of any unknown dynamics residual and feedback gains defining the adaptive control law. In Chapter 5, we generalize classical adaptive control concepts, namely stable concurrent estimation and certainty-equivalent cancellation of uncertain dynamics terms, to the robust model predictive control setting. Our approach allows us to couple classic online adaptation schemes, parameter uncertainty bounds, and robust model predictive control feedback that maintains safety through state and input constraint satisfaction, while decreasing conservatism in our uncertainty bounds over time. We conclude this thesis with a discussion of possible future avenues of work towards better synergy between learning modules in the autonomy stack and online closed-loop deployment objectives.
Convergent dopamine and serotonin reward signals govern associative learning
Keywords: Medicine.

In mammals, midbrain dopamine (DA) neurons in the ventral tegmental area (VTA) have long been thought to play a critical role in pavlovian learning because they respond to rewards, acquire responses to reward predictive cues, and project to limbic structures important for motivated behaviors. Now, there is mounting evidence that serotonin (5-hydroxytryptamine; 5HT) neurons in the dorsal raphe (DR) share these properties. This suggests limbic circuits are under coordinated neuromodulatory control as animals form new associations, and begs the question: how are converging DA and 5HT signals integrated to drive learning? In Chapter 1, I review current hypotheses about the functions of the DA and 5HT systems in reward learning, both individually and together. In Chapter 2, I present original research beginning with the development of a double transgenic model enabling simultaneous genetic access to the brain's DA and 5HT systems in mice. Neural circuit tracing with these mice identified the nucleus accumbens posterior medial shell as the limbic area with the greatest density of converging VTA DA and DR 5HT inputs. VTA DA axons in this target region were excited by rewards while DR 5HT axons were inhibited. Loss-of-function manipulations blunting both of these reward responses together, but not either one alone, disrupted learning and reduced reward consumption without being inherently aversive. Gain-of-function manipulations reproducing both reward responses, but not either one alone, were sufficient to drive acquisition of new cue-reward associations. Taken together, these data show that striatal integration of convergent DA and 5HT reward signals governs learning by gating reinforcement. Finally, in Chapter 3 I provide a discussion of these findings, potential mechanisms, and future directions
Convex neural networks
Keywords: Engineering.
Electrical Engineering.

Neural networks have made tremendous advancements in a variety of machine learning tasks across different fields. Typically, neural networks have relied on heuristically optimizing a non-convex objective, raising doubts into their transparency, efficiency, and empirical performance. In this thesis, we show that a wide variety of neural network architectures are amenable to convex optimization, meaning that their non-convex objectives can be reformulated as convex optimization problems using semi-infinite dual formulations. We first show that for two-layer fully connected neural networks with ReLU activations, the optimization problem is convex and demonstrates a unique link to copositive programming, with a regularizer which promotes both sparsity in the number of activation patterns used in the network, and sparsity in the number of neurons that are active for each activation pattern. We show that this formulation admits closed-form solutions in certain data regimes, and use copositive programming to relax the problem into one that is polynomial-time in the problem dimensions for data matrices of a fixed rank. We show that solving the convex reformulation results in a better solution than that found by heuristic algorithms such as gradient descent applied to the original non-convex objective. In the rest of this thesis, we explore different neural network architectures and training regimes which pose new challenges to the convex optimization formulation. We show that for convolutional neural networks and transformer architectures, the optimization problem also admits a convex reformulation. We also show that for neural networks with batch normalization and generative adversarial networks, the same convex reformulation techniques can disentangle uninterpretable aspects of non-convex optimization and admit faster and more robust solutions to practical problems in the field. Finally, we show that these approaches can be scaled to deeper networks using a Burer-Monteiro factorization of the convex objective which maintains convex guarantees but allows for layerwise stacking convex sub-networks in a scalable fashion.
Convex optimization for neural networks
Keywords: Engineering.
Electrical Engineering.

Due to the non-convex nature of training Deep Neural Network (DNN) models, their effectiveness relies on the use of non-convex optimization heuristics. Traditional methods for training DNNs often require costly empirical methods to produce successful models and do not have a clear theoretical foundation. In this thesis, we examine the use of convex optimization theory to improve the training of neural networks and provide a better interpretation of their optimal weights. In this thesis, we focus on two-layer neural networks with piecewise linear activations and show that they can be formulated as finite-dimensional convex programs with a regularization term that promotes sparsity, which is a variant of group Lasso. We first utilize semi-infinite programming theory to prove strong duality for finite-width neural networks and then describe these architectures equivalently as high dimensional convex models. Remarkably, the worst-case complexity to solve the convex program is polynomial in the number of samples and number of neurons when the rank of the data matrix is bounded, which is the case in convolutional networks. To extend our method to training data of arbitrary rank, we develop a novel polynomial-time approximation scheme based on zonotope subsampling that comes with a guaranteed approximation ratio. Our convex models can be trained using standard convex solvers without resorting to heuristics or extensive hyper-parameter tuning unlike non-convex methods. Due to the convexity, optimizer hyperparameters such as initialization, batch sizes, and step size schedules have no effect on the final model. Through extensive numerical experiments, we show that convex models can outperform traditional non-convex methods and are not sensitive to optimizer hyperparameters. In the remaining parts of the thesis, we first extend the analysis to certain standard two and three-layer Convolutional Neural Networks (CNNs) can be globally optimized in fully polynomial time. Unlike the fully connected networks studied in the first part, we prove that these equivalent characterizations of CNNs have fully polynomial complexity in all input dimensions without resorting to any approximation techniques and therefore enjoy significant computational complexity improvements. We then discuss extensions of our convex analysis to various neural network architectures including vector output networks, batch normalization, Generative Adversarial Networks (GANs), deeper architectures, and threshold networks
Coordination of distributed energy resources for distribution grid reliability
Keywords: Engineering.
Electrical Engineering.

Climate change concerns and economics are driving higher electrification and the adoption of distributed energy resources, such as rooftop solar photovoltaic, small scale battery storage, and new electricity consuming devices such as EV chargers, electric heat pumps, and water heaters, as part of an effort to reduce dependency on fossil fuels. Additionally, advances in information technology have enabled the grid to be increasingly observable and controllable with the widespread installation of new sensors and smart devices. While these changes have profound effects on all aspects of the power grid, we focus on the impacts of electrification and distributed renewable generation on the consumer side, leading to the rapid adoption of DERs. In particular, we look at how the increase in electrification including EV chargers, will cause significant increase in load which can lead to transformer overloading and how excess energy generated by rooftop solar and other DERs can be fed back into the grid, which can cause voltage and transformer problems. It has been projected that combating these challenges of increasing electrification and DERs would require costly infrastructure upgrades and / or the development of novel DER coordination schemes that jointly control DERs across a distribution grid, and electricity tariff structures and incentives. The controllability and flexibility of DERs, create an opportunity to coordinate their operation to reduce energy and grid upgrade costs while protecting local grid infrastructure assets such as transformers and maintaining grid reliability. However, effective integration and coordination is difficult because it requires the culmination of many consumer preferences, cyber-physical constraints, scalability, and various communication protocols and control access constraints. We use a power flow driven simulation and optimization methodology to quantify the adverse impacts of projected increases in DER penetration and electrification on distribution grids under different DER control schemes and electricity tariff structures. We describe a methodology for simulation of the grid and its devices including a quantification of the performance limits on DER coordination with two bookend controllers. A local only controller that represents the lower end of grid reliability performance for DER coordination and a perfect foresight oracle controller that represents the upper end of grid reliability performance. Then, we discuss the challenges of an implementable scheme and present a DER coordination scheme via day-ahead demand and supply power bounds that satisfies all the criteria for an implementable scheme.
Coronavirus emergence and plasmodium parasite prelude : unraveling first infections
Keywords: Medicine.
Microbiology and Immunology.

This dissertation embarks on a comprehensive journey to explore and contrast the T cell responses to two distinct infectious diseases: COVID-19 and malaria. By delving into the immunological intricacies of these diseases, this research aims to shed light on the critical roles T cells play in the immune response to both a novel pandemic and an ancient endemic disease. The second chapter presents insights into the dynamics of CD4+ T cell responses following SARS-CoV-2 infection, highlighting a shift in cytokine production patterns and underscoring the potential role of TNF$\alpha$-producing CD4+ T cells in sustaining post-infection immunity. The third chapter focuses on malaria's impact on immune responses during pregnancy, particularly among primigravid women, revealing the modulation of immune responses by Type 1 regulatory (Tr1) cells. The fourth chapter explores genetic factors influencing malaria susceptibility through a study on Diversity Outbred mice, identifying specific loci associated with varying disease outcomes. This thesis integrates these findings to enhance our understanding of T cell correlates and genetic factors that shape immune responses during initial infections. By contrasting the historical context of malaria with the contemporary challenge of COVID-19, the research provides a unique perspective on the evolution of immune responses and their implications. This comprehensive study not only advances our knowledge of immunology but also proposes therapeutic insights, aiming to inform future targeted interventions in the fight against infectious diseases.
Correlation clustering at scale
Keywords: Engineering.

Correlation clustering is a useful method in machine learning and data analysis that aims to partition data into groups based on the notion of pairwise similarity. Given a collection of objects and a labeling of every pair of objects as either "similar" or "dissimilar", the goal is to find a partitioning of the objects such that similar pairs tend to be clustered together while dissimilar pairs tend not to. Correlation clustering has become desirable in many applications ranging from locating boundaries in digital images to detecting communities in social networks. In many of these applications, the order of magnitude of the input significantly exceeds the resources available to the algorithm such as time and space, which has led to increasing demand for designing efficient algorithms at large scale. In this thesis, we study the problem of correlation clustering in models of efficient computation with large-scale data and propose new algorithms with much improved guarantees. We first present a (3+epsilon)-approximate parallel algorithm for minimizing disagreements with labels that runs in O(1/epsilon) rounds. The approximation ratio almost matches a natural target of 3 for combinatorial algorithms. Then, we give a single-pass 5-approximate streaming algorithm and show that the approximation analysis is tight. Both algorithms are simple to implement and have implications in various models including massively parallel computation, streaming, distributed local model, local computation algorithms, and fully dynamic algorithms
Cosmic shear, or atmosphere? Modeling the atmosphere for dark energy measurements with the Vera C. Rubin Observatory
Keywords: Humanities and Sciences.
Applied Physics.

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will image tens of billions of galaxies over ten years--a 100 times increase over previous surveys--enabling the most precise measurements of dark energy properties to date. Realizing the full potential of this powerful dataset requires an unprecedented understanding of observational effects that, if uncorrected, can bias the science results. One such source is the blurring of images due to optical turbulence in the atmosphere, which dominates the point-spread function (PSF) for ground-based instruments. This atmospheric effect imprints spatially correlated noise on scales (and with amplitudes) similar to the cosmological signal we will study: the spatial correlation of positions and shapes of galaxies on the plane of the sky due to gravitational lensing of light by the dark matter in the Universe. High-fidelity simulated astronomical images are an important tool in developing and measuring the performance of image-processing algorithms that will be needed to accurately and precisely account for sources of correlated noise such as the atmospheric PSF. This thesis presents a new simulation tool, psf-weather-station, that allows us to study and model the dependence of correlations in the atmospheric PSF on weather conditions at any observatory by leveraging data from weather forecasting models. We use this tool to simulate the atmospheric PSF predicted for the Vera C. Rubin Observatory in Chile and make quantitative predictions of two-point correlation functions in the size and shape of the PSF that are used in analyses of cosmic shear. We also explore the temporal behaviour of the atmospheric PSF using high-resolution speckle images of stars, recorded near Rubin Observatory, and simulate similar observations using pfs-weather-station.
Creating Custom Optical Potentials with Spatial Light Modulators
Keywords: Physics
Physics
Spatial Light Modulators
SLM
Bottle Beams
Optical Vortices
Text
Thesis

Modern atomic physics experiments use precisely engineered optical intensity profiles to trap and manipulate atoms. These profiles are created by optical systems based on spatial light modulators (SLMs) and acousto-optic deflectors. SLMs can, in theory, produce nearly arbitrary two-dimensional and select three-dimensional intensity profiles though technical challenges have largely limited SLMs to the formation of optical tweezer arrays. In this thesis, I present computational and experimental methods to greatly expand the set of achievable intensity profiles. For 2D profiles, finding an optimal phase distribution for the SLM is the primary challenge; I demonstrate a novel phase-smoothing approach to eliminate optical vortices and allow for better convergence. I also show results from a camera feedback procedure that corrects for aberrations not considered in the computational model. Finally, I present results from two methods of generating arrays of 3D bottle beams: spliced phase gradients and acousto-optics. These results highlight the viability of our methods for future use in neutral atom experiments.
Crossing the empires : travel poetry in Southern Song China (1127-1210)
Keywords: Humanities and Sciences.
East Asian Languages and Cultures.

This first study in any language on the emissary poetry of the Southern Song explores the relationship between politics and travel poetry by Song officials from the standpoint of Song-Jin relations. It challenges the existing scholarship which claims that there was nationalism in the Southern Song. More specifically, through close reading, the present study shows that there was no shared consciousness among the Southern Song emissaries as it varied from mission to mission, person to person. Furthermore, this study reveals that irredentism or revanchism -- often viewed as a central feature of nationalism -- is but a particular version of the policy of huifu. Huifu meant different things to the emperorship and the officials at different points of Song-Jin relations. And the dream travels of Lu You in the last chapter highlight that irredentism was also changing and shifting at an individual level. To such an extent that this most famous irredentist of the Southern Song gave up his irredentism towards the end of his life. This study does show, however, the loyalty of the Song officials. But with a distinction between loyalty to whom and loyalty to what. While some showed allegiance to the emperorship, some showed allegiance to the Song dynasty. The entity that commanded their loyalty was not an imagined community but the Song empire, which was seriously challenged by the Jurchen Jin, its more powerful neighbor in the north. Finally, this study demonstrates that travel poetry informed by foreign relations and domestic politics -- like ancestor's instructions and Confucian thought -- is also an integral part of the Song political culture
Cryogenic single-molecule active control microscopy with fluorescent proteins
Keywords: Humanities and Sciences.
Chemistry.

Superresolution microscopy is a powerful optical technique for studying molecules and structures within biological cells at size scales below the optical diffraction limit of ~200-300 nm. Performing superresolution imaging on samples maintained at cryogenic temperatures provides several advantages: reduced photobleaching resulting in improved localization precisions, the use of plunge-freezing as a chemical-free fixation technique, and the potential of correlative imaging with cryogenic electron tomography (cryoET). However, single-molecule superresolution methods such as PALM and STORM are difficult to implement in frozen samples due to the limited availability of fluorophores with access to efficient active control mechanisms at low temperatures. The work presented in this dissertation establishes a workflow for cryogenic fluorescence imaging compatible with subsequent cryoET and details our search for control over the emissive states of two fluorescent proteins, PAmKate and mApple, under cryogenic conditions