VictorJuiz/thesis huggingface dataset:
Characterizing nuclear remodeling during budding yeast meiosis

The nucleus, the defining organelle of the eukaryotic cell, must be remodeled during every cell division in order to accommodate the division of genetic material. While this remodeling has been extensively studied during mitosis, it remains poorly understood how the nucleus is remodeled during meiosis. In this study, we provide a comprehensive analysis of nuclear behavior during meiosis in the model organism budding yeast. First, we find that the nuclear envelope undergoes a five-way division, forming a nuclear-envelope bound compartment that is excluded from gametes. This compartment, termed the Gametogenesis Uninherited Nuclear Compartment or GUNC, contains nuclear pore complexes (NPCs) in all cells and age-induced damage in old cells. The material sequestered to the GUNC is subsequently eliminated by release of vacuolar proteases, establishing this remodeling event as a novel nuclear quality control mechanism that contributes to meiotic cellular rejuvenation. Second, we find that the NPC undergoes two mechanistically-distinct meiotic remodeling events: partial nuclear basket detachment during meiosis I and full nuclear basket detachment during meiosis II. Meiosis I detachment, which involves Nup60 and its binding partner Nup2, is driven by Polo kinase-dependent phosphorylation of Nup60 at its binding interface with the NPC core. Notably, this remodeling event also occurs in the distantly related Schizosaccharomyces pombe, suggesting basket detachment involves conserved nuclear basket organizational principles and fulfills an important function. Finally, we find that the nuclear permeability barrier is transiently disrupted during meiosis II, driving intermixing of the cytoplasm and nucleoplasm. Since the nuclear envelope stays intact throughout meiosis, regulation of nuclear transport machinery is likely involved in this event. Several meiotic regulators disrupt barrier loss or return, although their precise mechanistic contributions remain unclear. In all, this work establishes the nuclear periphery as a highly dynamic and regulated entity during budding yeast meiosis. Studying the budding yeast meiotic nucleus will continue to improve our understanding of nuclear organization and its contributions to cellular health for years to come.

Advisor: Ünal, Elçin
Distributed Pressure Sensing for Chord Control of Airplane Roll

Comparing airplanes to birds, there is a considerable difference in the way each achieves flight. Airplanes are rigid structures with a few sensors and actuators placed in optimized spots on the structure. Birds, on the other hand, use biological sensors throughout their bodies as well as are able to contort their wings and body to get the desired effects to fly. In this dissertation, I introduce a novel method inspired by birds for achieving controlled flight of an airplane./nFirst, I look at the wing in spanwise discretized sections. Each section can be analyzed as an airfoil and has a set of pressure sensors to retrieve aerodynamic parameters: angle of attack and freestream velocity. Three methods to retrieve these parameters are evaluated for accuracy and computation speed. The best method is chosen for the remainder of this dissertation. Additionally, each section has a flap that can alter the lift, drag and pitching moment resulting from the airflow going over the section. I propose an attitude control loop to integrate the new knowledge of the localized airflow of each wing section across the entire wing to more optimally move the actuators and meet the desired attitude./nSubsequently, I demonstrate the proposed distributed sensing and control method working through tests on the roll dynamics of a wing. These tests show that the new method proposed in this dissertation detects localized airflow that would not be noticed by traditional sensing methods, follows a reference roll signal in a nominal condition more efficiently, and better rejects localized airflow disturbances./nLastly, I present a novel design for sensitive thin-film capacitive pressure sensors that can be distributed across aerodynamic surfaces without the need of embedding pressure sensors into the wing structure.

Advisor: Fearing, Ronald S
Borrelli, Francesco
Three Problems in the Control and Identification of Structured Linear Systems

We study three problems in the control and identification of structured linear systems. Thestructure is first manifested as sparsity pattern constraints on the system or control matrices, which complicate the feasible set of the optimal decentralized control problem. We find that the feasible set can be not only disconnected but also have a large number of connected components, which greatly limits the application of local search optimization algorithms. The issue of connectivity is addressed in the second problem, where we design homotopy paths that reduce the number of local minima of the optimal decentralized control problem. Finally, we study an identification scheme based on L-1 optimization, where the system states are subject to attacks which propagate over time. The structural constraint, which appears as inequalities involving the states and control inputs, will lead to sufficient conditions for the recovery of system matrices.

Advisor: Lavaeiyanesi, Javad
Energy Management in Microgrids for Electricity Access

This thesis responds to the related imperatives of transitioning to low-carbon electricity systems and increasing global access to energy. It shows that microgrids and decentralized electricity systems are economically and technically capable of providing high levels of electricity access, and argues that incorporating active participation of electricity ``prosumers’’ into energy management systems enables more efficient electricity resource management./nChapter 1 quantifies the tradeoffs between costs and electricity for autonomous solar and battery systems across sub-Saharan Africa, finding that on average these autonomous systems can achieve high levels of reliability at a cost of on the order of 10 USD cents per ‘9’ of reliability. Moreover, it shows that these costs could drop to as low as 3 cents per 9 as battery costs decline, and that decentralized systems are cost-competitive with legacy grids across much of the continent./nChapter 2 proposes a load management system to manage electricity consumption in community microgrids with solar photovoltaics and battery storage while accounting for forecast uncertainty. It uses stochastic, model-predictive control techniques to set consumption limits during periods of low solar availability and high-demand. Simulation experiments show the management technique improves system reliability and consumer benefits from electricity through fewer interruptions and better electricity availability to high value uses./nChapter 3 studies optimal pricing and peer-to-peer energy trading systems in microgrids with 100\% renewable energy sources. It promotes a utility-maximization framework from which prices arise from exchanging electricity under scarcity, in contrast to standard marginal-cost based pricing that breaks down in 100\% renewable systems. It further proposes a negotiation algorithm for peer-to-peer energy transactions and proves its convergence to optimal exchanges./nChapter 4 extends the algorithm from Chapter 3 to a more broadly applicable system for optimizing power exchanges in microgrids and larger power grids in using forward markets and real-time controls. This approach, based on a decentralized optimization technique known as the Alternating Direction Method of Multipliers (ADMM), uses price-based coordination and independent agents in an iterative bidding procedure. Its equilibrium is a welfare-maximizing dispatch that solves the non-linear and non-convex power flow equations. This system preserves individual privacy, efficiently incorporates network congestion and voltage constraints, is highly scalable, and is robust in practice to model error. In addition to the forward market, the chapter introduces an agent-based feedback control system that continues to optimize power exchanges in real time./nThe thesis concludes with a brief summary and directions for future research.

Advisor: Callaway, Duncan S
A Reproducing Kernel Particle Method Mesh-free Numerical Framework for Large-deformation Computational Solid Mechanics and Adaptivity

A mesh-free dynamic physics simulation framework built on the Reproducing Kernel Particle Method (RKPM) is developed for explicit dynamic computational solid mechanics with large strains and high strain rates. Potential applications include simulation of armor and shielding, soft body impacts, and projectile behavior. A quasi-static variation of the patch test is used for numerical validation and a selected Taylor bar impact experiment is used as a benchmark problem for verification.

Advisor: Zohdi, Tarek I
Phase Separation as a Model of Nucleoprotein Organization

Recent work has revealed the importance of liquid-liquid phase separation (LLPS) as a mechanism to organize cells through the formation of non-membrane-bound cellular compartments such as nucleoli, Cajal bodies, and stress granules. These compartments concentrate specific proteins and nucleic acids through weak, noncovalent interactions. I investigated phase separation as an organizing principle for both eukaryotic cells and viruses. In particular, I have studied the shelterin protein complex, which binds and protects human telomeres. Telomeres form unique nuclear compartments that prevent degradation and fusion of chromosome ends by recruiting shelterin proteins and regulating access of DNA damage repair factors. To understand how these dynamic components protect chromosome ends, I combine in vivo biophysical interrogation and in vitro reconstitution of human shelterin. I show that shelterin components form multicomponent liquid condensates with selective biomolecular partitioning on telomeric DNA. Tethering and anomalous diffusion prevent multiple telomeres from coalescing into a single condensate in mammalian cells. However, telomeres can coalesce when brought into contact via an optogenetic approach. TRF1 and TRF2 subunits of shelterin drive phase separation, and their N-terminal domains specify interactions with telomeric DNA in vitro. Telomeric condensates selectively recruit telomere-associated factors and regulate access of DNA damage repair factors. I propose that shelterin mediates phase separation of telomeric chromatin, which underlies the dynamic yet persistent nature of the end-protection mechanism./nMy work also addressed how interactions between the nucleocapsid (N) protein and viral RNA in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) drive LLPS. SARS-CoV-2 infection causes COVID-19, a pandemic that seriously threatens global health. SARS-CoV-2 propagates by packaging its RNA genome into membrane enclosures in host cells. Packaging of the viral genome into the nascent virion is mediated by the N protein, but the underlying mechanism is not fully understood. I found that the N protein forms biomolecular condensates with viral genomic RNA both in vitro and in mammalian cells. While the N protein forms spherical assemblies with homopolymeric RNA substrates that do not form base-pairing interactions, it forms asymmetric condensates with viral RNA strands. Cross-linking mass spectrometry identifies a region that mediates interactions between N proteins in condensates, and truncation of this region disrupts phase separation. I also identified small molecules that alter the formation of N protein condensates and inhibit the proliferation of SARS-CoV-2 in infected cells. These results suggest that the N protein may promote biomolecular condensation to package the SARS-CoV-2 RNA genome into a viral particle.

Advisor: Yildiz, Ahmet
Stochastic Optimization: Complexity-Based Analysis and Development Engineering Applications

This dissertation work combines two lines of work related to stochastic optimization, one focused on theoretical contributions to the field, and the other focused on modelling and philosophical contributions to the field. In the first line of work, we contribute to the theoretical underpinnings of two models that draw from data analysis, mathematical and statistical modeling, and optimization. In particular, there are several classes of machine learning problems which interface with stochastics and optimization, and, with the use of stochastic process theory and associated notions of complexity, we can recharacterize some of the theoretical expectations of these problems, which importantly model uncertainty. The improved results are gained from problem reformulations, improved analytical techniques, and a concerted effort to use the most recent advances in complexity analysis and their associated generalizations, and has implications for the construction and analysis of societal scale models. In the second line of work, we demonstrate why the techniques in stochastic optimization help form a mathematical and statistical basis that can solve large-scale societal problems over time, with precise engineering intervention focus. The main model researched is the Principal Agent Model (PAM). Considering sectors of the social economy as the principal and communities of people as the agents, we utilize development engineering and the associated economics of global poverty reduction to unveil the concept of Optimal Intervention Theory as a means to explore, adapt, and activate tractable solutions in specially-constrained environments. By tractable here, we mean interventions that last long beyond the intervention period, according to the education-, resource-, access-, and network-bases interventions afforded the impacted populations, especially those concerned with international development and global poverty alleviation.

Advisor: Aswani, Anil J
Towards Trustworthy Machine Learning

Real-world applications of machine learning often have complex objectives and safety-critical constraints. Contemporary machine learning systems excel at achieving high average-case performance at tasks with simple procedurally specified objectives, but they struggle at many more demanding real-world tasks. In this thesis, we work towards developing trustworthy machine learning systems that understand human values and reliably optimize them./nMachine learning’s key insight was that it is often easier to learn an algorithm than to write it down directly—yet many machine learning systems still have a hard-coded, procedurally specified objective. The field of reward learning applies this insight to instead learn the objective itself. As there is a many-to-one mapping between reward functions and objectives, we start by introducing the notion of equivalence classes consisting of reward functions that specify the same objective./nIn the first part of the dissertation, we apply this notion of equivalence classes to three distinct settings. First, we study reward function identifiability: what set of reward functions is compatible with the data? We start by categorizing the equivalence classes of reward functions that induce the same data. By comparing these to the aforementioned optimalpolicy equivalence class, we can determine whether a given data source provides sufficient information to recover the optimal policy./nSecond, we address the fundamental question of how similar or dissimilar two reward function equivalence classes are. We introduce a distance metric over these equivalence classes, the Equivalent-Policy Invariant Comparison (EPIC), and show rewards with low EPIC distance induce policies with similar returns even under different transition dynamics. Finally, weintroduce an interpretability method for reward function equivalence classes. The method selects the easiest to understand representative from the equivalence class, and then visualizes the representative function./nIn the second part of the dissertation, we study the adversarial robustness of models. We start by introducing a physically realistic threat model consisting of an adversarial policy acting in a multi-agent environment so as to create natural observations that are adversarial to the defender. We train the adversary using deep RL against a frozen state-of-the-artdefender that was trained via self-play to be robust to opponents. We find this attack reliably wins against state-of-the-art simulated robotics RL agents, and superhuman Go programs./nFinally, we investigate ways to improve agent robustness. We find adversarial training is ineffective, however population-based training offers hope as a partial defense: it does not prevent the attack, but it does increase the computational burden of the attacker. Using explicit planning also helps, as we find that defenders with large amounts of search are harder to exploit.

Advisor: Russell, Stuart J
Discrete Systems in Quantum and Statistical Mechanics

Here we consider operators in the physics literature and explore their discretized counterparts for a better understanding of their behavior. In chapter 2, we discretize a standard Hamiltonian model from quantum mechanics and, in this setting, develop a discretized Gelfand-Yaglom formula. From this discrete set up we are able to develop an alternative regularization for the determinant of a class of operators. We refer to this as the lattice-regularization./nIn chapters 3 and 4 we develop connections between the asymptotics of the inverse of two different operators: the Kasteleyn operator with interface and the Dirac operator with interface. The definition of these operators are given in their respective chapters. While the former is operator acting on a discrete space and the latter is acting on the plane, there are well established connections between the two. Moreover, in chapter 3, we give a complete picture of the asymptotics across the interface when one half of the lattice is weighted critically and the other half is weighted non-critically.

Advisor: Reshetikhin, Nicolai
Galilean Theory of Dispersion and Scattering for Kinetic Equations

This dissertation is constituted of two main parts and establishes a new approach in the study of the kinetic equations, which relies on the Galilean invariance of the inertial frames with respect to different moving observers. In the first part, we will study the kinetic equations independent of the specific structure of the interactions and solely under the assumptions of the conservation laws. We will introduce a new a-priori bound and a kinetic uncertainty principle and show that the uncertainty, relative to any observer, goes to infinity with time. Moreover, we will demonstrate estimates for the kinetic equations with analogies to the Morawetz and interaction Morawetz estimates based on the intuition of uncertainty and the introduction of the blind cones with respect to an observer. These estimates, together with the concept of blind cones, lead to a new formulation of dispersion for the both ensembles of particles and interactions, with the time average versus the ensemble average as the central motif of the formulation. In fact, as the uncertainty inevitably increases with time, particles will move away in a an asymptotically radial manner from any fixed observer and the interactions asymptotically tend to be grazing, thereby establishing a general notion of dispersion. In other words, relative to any observer and averaged over time, almost every particle moves away radially and almost every interaction is grazing. In the second part, we will develop a scattering theory for the kinetic equations in generality and show its application to the Boltzmann equation by introducing a novel estimate for the Maxwellian distribution based on its inherent Galilean invariance. In particular, we will demonstrate the existence, uniqueness and asymptotic completeness of the scattering solutions to the Boltzmann equation with hard spheres kernel defined over the whole space for initial values near the vacuum.

Advisor: Tataru, Daniel L
Synergy of Prediction and Control in Model-based Reinforcement Learning

Model-based reinforcement learning (MBRL) has often been touted for its potential to improve on the sample-efficiency, generalization, and safety of existing reinforcement learning algorithms.These model-based algorithms constrain the policy optimization during trial-and-error learning to include a structured representation of the environment dynamics. To date, the posited benefits have largely been left as directions for future work. This thesis attempts to illustrate the central mechanism in MBRL: how a learned dynamics model interacts with decision making. A better understanding of this interaction will point the field in the direction of enabling the posited benefits./nThis thesis encompasses the interaction of model-learning with decision making with respect to two central issues: compounding prediction errors and objective mismatch.The compounding error challenge emerges from accumulating errors on recursive passes of any one-step transition model. Most dynamics models are trained for single-step accuracy, which often results in models with substantial long-term prediction error. Additionally, the model being trained for accurate transitions need not guarantee high-performance policies on the downstream task. The lack of correlation between model and policy metrics in separate optimization is coined and studied as Objective Mismatch./nThese challenges are primarily studied in the context of sample-based model predictive control (MPC) algorithms, where the learned model is used to simulate trajectories and their resulting predicted rewards.To mitigate compounding error and objective mismatch, the trajectory-based dynamics model is a feedforward prediction parametrization containing a direct representation of time. This model represents one small, but important steps towards more useful dynamics models in model-based reinforcement learning. This thesis concludes with future directions on the synergy of prediction and control in MBRL, primarily focused on state-abstractions, temporal correlation, and future prediction methodologies.

Advisor: Pister, Kristofer S.J.