VictorJuiz/thesis huggingface dataset:
Specifications from Demonstrations: Learning, Teaching, and Control

This dissertation considers the problem of learning and teaching Boolean task specifications, such as automata, using demonstrations. The resulting framework bridges grammatical inference and maximum-entropy inverse reinforcement learning with applications in human-robot interaction, formal synthesis, and multi-task reinforcement learning./nIn the context of inverse reinforcement learning, Boolean task specifications are a class of sparse memory augmented rewards with explicit support for temporal and Boolean composition. These properties make task specifications immune to certain classes of reward hacking bugs that emerge from ad-hoc composition or perturbations to the dynamics. Unfortunately, the discrete nature of task specifications combined with an a-priori ignorance of what historical features are needed to encode the demonstrated task make existing approaches to learning rewards from demonstrations inapplicable./nIn the context of specification mining and grammatical inference, demonstrations provide an ergonomic and sample efficient means to communicate formal languages and specifications. For example, this dissertation enables a user to partially specify the desired behavior of a system as example demonstrations and then find an automata or program that explains the user's behavior. Conversely, by synthesizing pedagogic demonstrations, this dissertation enables communicating the nuances of a specification that may be hard to intuit from a formal description./nIn either case, this thesis contributes a collection of algorithms and theoretical machinery for systematically mitigating combinatorial explosions inherent in (1) finding specifications that explain an agent's behavior (2) finding pedagogic demonstrations that help humans infer the specification and (3) robustly predicting the behavior of an agent adhering to a specification.

Advisor: Seshia, Sanjit A
Mixed-Variable Multi-Objective Bayesian Optimization, Design-by-Morphing and their Applications

Fluid flows are non-intuitive. Even with years of experience, non-intuitive behavior of fluids can mean the optimal geometry of fluid machinery is surprising or even extreme (consider, for instance, the bulbous bow of a ship). Finding the optimal design of a hydrodynamic or aerodynamic surfaces is often impossible due to the expense of evaluating the cost functions (say, with computational fluid dynamics) needed to determine the performances of the flows that the surface controls. In addition, inherent limitations of the design space itself due to imposed geometric constraints, conventional parameterization methods, and user bias can restrict all of the designs within a chosen design space regardless of whether traditional optimization methods or newer, data-driven design algorithms with machine learning are used to search the design space./nThis dissertation presents two methodologies to address these difficulties: (1) Design-by-Morphing (DbM), a novel strategy for creating a design search space by morphing homeomorphic shapes to create a continuous and constraint-free design search space that can produce radical extrapolated shapes, something which is unique from existing design strategies; and (2) an optimization algorithm to search that space that uses a novel Mixed-variable, Multi-Objective Bayesian Optimization that we call MixMOBO, that can optimize such expensive, black-box problems with minimum number of functions calls. We apply these methodologues for optimization of several problems and present shape optimization of airfoils, draft-tubes for hydrokinetic turbines, and architected meta-materials. In all cases, we show significantly improved and radical designs./nChapter One of this thesis focuses on the details of the MixMOBO algorithm, the first mixed-variable, multi-objective Bayesian optimization algorithm. MixMOBO outperforms existing algorithms for mixed-variable problems. It details HedgeMO strategy for hedging acquisition function portfolios for multi-objective problems. MixMOBO is then applied for optimization of strain energy density of an architected meta-material structure with categorical variables. From a design space of 8.5 billion possible candidates, our algorithm is able to optimize the design space with only 250 function evaluation and achieve 10^4 times improvement in strain energy density over existing structures [1,2]. Chapter Two focuses on applying MixMOBO for design of Cauchy-Symmetric architected meta-material structures. With only 69 function calls, MixMOBO is able to find such a structure from a design space of 10^7 possible structures. Chapter Three demonstrates the use of Design-by-Morphing for optimization of airfoils. We show that with just 25 baseline shapes, we are able to reproduce the UIUC airfoil database with high fidelity and optimize this space to create aerodynamically superior and safer airfoils [3]. Chapter Four focuses on application of design of a draft tube for a hydrokinetic turbine to maximize pressure recovery at the exit of the turbine [4].

Advisor: Marcus, Philip S
Sublogarithmic-Transexponential Series

This thesis is motivated by the open question of whether there are transexponential o-minimal structures. As a candidate for a transexponential o-minimal structure, we suggest $\mathbb{R}_{\mathrm{an},\exp}$ expanded with new symbols for a transexponential function, its derivatives, and their compositional inverses, which we call $\mathbb{R}_{\mathrm{transexp}}$. The main result of the thesis is that the germs at $+\infty$ of $\mathbb{R}_{\mathrm{transexp}}$-terms are ordered and thus form a Hardy field. If $\mathbb{R}_{\mathrm{transexp}}$ is shown to have quantifier elimination in the future, then o-minimality would follow. Chapter 1 provides background on the open problem and more information on $\mathbb{R}_{\mathrm{transexp}}$./nThe work of the thesis is to adapt the construction of the field of logarithmic-exponential series in Logarithmic-Exponential Series to build an ordered differential field of sublogarithmic-transexponential series. The sublogarithmic-transexponential series field is constructed to embed the germs of $\mathbb{R}_{\mathrm{transexp}}$-terms, showing that they are ordered. The challenge is to gradually build up the field of series so that we know how the partial structure should be ordered at each stage. We discuss how the ordering quickly becomes complicated even for relatively simple finite sums in Chapter 2./nThe construction of the sublogarithmic-transexponential series can be divided into three parts. First, in Chapter 3, we prove that the group ring of certain kinds of finite sums is ordered by giving an algorithm to determine the sign on a sum and showing the algorithm terminates. Next, in Chapter 4, we adapt the construction of the logarithmic-exponential series to build a field of series closed under log, exp, and restricted analytic functions, starting from a field of coefficients and group of monomials satisfying certain assumption. Finally, in Chapter 5, we iterate the construction from Chapter 4 to build the full field of sublogarithmic-transexponential series. We also define a derivation that works like "differentiation with respect to the formal variable of the series."

Advisor: Scanlon, Thomas
Selected Topics in Dispersed Multiphase Flow Transport

The COVID-19 pandemic heightened the interest in particle-laden turbulent jets generated by breathing, talking, coughing, and sneezing, and how these can contribute to disease transmission. Predicting the transport distance of the expelled droplets remains a critical, and still open, question. In the first segment of my work, I study the effect of ejection geometry and environmental boundary conditions on the transport of water density-matched particles. The study of particle transport from a canonical jet geometry illustrates the influence of secondary flows from the environmental boundary conditions (e.g. thermal gradients and wall effects) on particle dispersal. Subsequent experiments on the particle transport from a repeatable cough generator reveal that vortex structures created by the mouth and tongue structure have a large role in dispersal, due to the ejection of particles from regions of high vorticity./nIn the second segment of my work, I study bubbly flows, in which the dispersed and carrier fluid phases are reversed. This flipped density ratio causes bubbles to accumulate in regions of high vorticity. Bubble transport is studied in the context of flow over a cylinder, for which the induced vibration is an important design consideration in devices from flow meters to nuclear reactors. While past researchers have shown that introducing bubbles to the flow can decrease vibration amplitude while increasing shedding frequency, the nonlinear shift in shedding frequency with bubble size has not been explained. Through experimental and numerical study of size-dependent bubble transport, I provide insight into the mechanismcausing this change in shedding frequency./nThe study of both projects in tandem allows for the exploration of the effect of preferential accumulation/ejection on dispersed multiphase transport. In addition to complementary flow behaviour, the study of interchanged dispersed/carrier phases necessitates the development of highly specialized measurement techniques. I hope that the methods developed in this work see increased use in literature when developing validation datasets, as I look to challenge some of the common assumptions made in existing multiphase transport studies. A common theme through both projects is that typically second-order effects (e.g. spanwise velocity differences) have a significant influence on dispersed multiphase transport. As dispersed multiphase transport draws increased research interest, I hope that this work illustrates that (until proven otherwise) the collective understanding of the dominant mechanisms that exist for the single phase flow do not necessarily apply to multiphase transport.

Advisor: Makiharju, Simo A
Investigating Systemic Aging and its Effects on Aged Tissues by Utilizing a Small Animal Blood Exchange Model

Aging is characterized as a progressively worsening loop where an accumulation of aberrant molecules and cells lead to a plethora of ailments. Tissue homeostasis gradually falters leading to inadequate resident stem cell responses, persistent inflammation, fibrosis, and metabolic derangements. Furthermore, organ secretome profiles of aging individuals shift from factors that bolster tissue health and regeneration to a milieu that progressively impairs tissue function. These harmful factors accumulate in the blood and are distributed throughout the body. Heterochronic parabiosis experiments have shown that the systemic environment can impact organ regeneration. Young blood can rejuvenate the performance of aged tissue stem cells, whereas old blood can negatively affect those of the young. This work strongly reinforces the paradigm which maintains that mammalian aging is plastic, yet the translatability of this work remains elusive. It is also unclear whether young blood factors are necessary for rejuvenation. In this dissertation, a preclinical model that mimics plasmapheresis was developed to enable immediate translation to therapies for aged humans. Plasmapheresis is an FDA-approved treatment modality where plasma is extracorporeally separated from the blood, replaced with a physiologic fluid, and transfused back into the bloodstream. We demonstrate that the dilution of aged blood plasma rejuvenated the muscle and brain. The plasma dilution studies introduced a novel paradigm which holds that young blood factors may not be essential for rejuvenation; a neutral-age physiological fluid could suffice in this regard. These works broaden our understanding of systemic aging and suggest a novel repositioning of plasmapheresis to improve the health span of aged people.

Advisor: Conboy, Irina M.
Markov processes and variational problems

In this thesis, we study the statistical properties of non-linear transforms of Markov processes.These transforms are defined via variational formulas, and arise in various fields such as statistics, mathematical finance, convex analysis, statistical mechanics and hydrodynamic turbulence. In particular, we will focus on two sets of problems. The first problem that is addressed in Chapter 2, concerns the study of the Lipschitz minorant of the sample paths of a L´evy process. The study of this minorant was initiated by Abramson and Evans, but here we shed a light on its excursion structure away from its contact set. When the L´evy process is a Brownian motion with drift, an explicit path decomposition of these excursions is given, together with the decomposition of the semimartingales in the progressive enlargement of the canonical filtration by the first positive point in the contact set. In the second set of problems, we will consider physical solutions (also called entropy solutions) to scalar conservation laws (or equivalently Hamilton-Jacobi equations) with random initial data. In Chapter 3, we investigate the distribution of the solutions at later times in the one-dimensional case and when the initial data is a Brownian white noise for any general convex Hamiltonian. This settles a conjecture of Menon and Srinivasan and extends Groeneboom’s result for the Burgers case. In Chapter 4, we consider the higher dimensional case and focus in particular on the planar case. We construct a family of random convex piecewise linear functions which are the dimension 2 analogue of the anti-derivative of pure-jump Markov processes. At the heart of this construction is a novel class of kinetic equations. The invariance of this class of processes under the flow of Hamilton-Jacobi equations is also discussed.

Advisor: Evans, Steven
Rezakhanlou, Fraydoun
Useful interpretability for real-world machine learning

The recent surge in highly successful, but opaque, machine-learning models has given rise to a dire need for interpretability. This work addresses the problem of interpretability with novel definitions, methodology, and scientific investigations, ensuring that interpretations are useful by grounding them in the context of real-world problems and audiences. We begin by defining what we mean by interpretability and some desiderata surrounding it, emphasizing the underappreciated role of context. We then dive into novel methods for interpreting/improving neural network models, focusing on how to best score, use, and distill interactions. Next, we turn from neural networks to relatively simple rule-based models, where we investigate how to improve predictive performance while maintaining an extremely concise model. Finally, we conclude with work on open-source software and data for facilitating interpretable data science. In each case, we dive into a specific context which motivates the proposed methodology, ranging from cosmology to cell biology to medicine. Code for everything is available at github.com/csinva.

Advisor: Yu, Bin
Some Problems on the Convex Geometry of Probability Measures

This thesis consists of three main topics in which we explore the geometry and other features of certain convex sets arising in probabilistic contexts./nWe first consider the set of laws of $K_n$, the number of distinct values among the first $n$ terms of a sequence, for infinite exchangeable sequences of random variables $(X_1,X_2,\ldots)$. We prove for $n=3$ that the extreme points of the convex set of all possible laws of $K_3$ are those derived from i.i.d. sampling from discrete uniform distributions and the limit case with $P(K_3=3)=1$. We also consider the problem in higher dimensions and variants of the problem for finite exchangeable sequences and exchangeable random partitions./nSecond, we introduce the notion of a coherent pair of random variables, or two conditional probabilities of the same event, and study the convex set of laws on $[0,1]^2$ arising in this manner. We classify all extreme laws with a certain restriction on the support. We also discover a large class of extreme laws with finite and countably infinite support./nThird, we study the convex set of polynomial probability densities on $[0,1]$ of degree at most $n$. We review some known results, including characterization of the extreme points, a representation theorem, properties of the Bernstein polynomial basis, and the Lorentz degree which measures in some sense the representability of positive polynomials in the Bernstein basis. We map out the geometry for $n=2$, consider the uniform random sampling model, and compute the upper envelope for this set of polynomials.

Advisor: Pitman, Jim
Coordinating Deception in the Classroom: A Developmental Study of Child and Adolescent Reasoning and Conceptions of Authority Dictates and Deception in Educational Contexts Involving Harm, Sharing, and Inclusion

In social situations where power and authority hierarchies are firmly established, such as in a K-12 school, expectations of compliance are often seen as an inherent obligation of those with relatively less power. However, it is precisely because of this imbalance of power that compliance and deception become relevant when commands given are in opposition to what an individual judges to be morally right. How do individuals reason about similar acts when they may lead to conditions of more or less equality? How are authority dictates and deception conceptualized, endorsed, or resisted when such actions alter this balance further?This study examined developmental changes in children’s judgments and modes of reasoning about students’ acts of compliance with, and deception of, authority dictates in educational settings, where the student and teacher’s goals were in opposition. In each situation, the student and teacher’s actions and goals were in conflict in that they promoted equality or inequality among students. Sixty-three participants from two age groups (7-8, 15-16) were interviewed about hypothetical situations where the student shared lunch with, included, or avoided harming other students, or did so towards white but not black students. After the initial student action, a teacher intervened and commanded that the student stop their behavior, and the student complied. Once the teacher departed, the student deceptively resumed their original behavior out of sight of the teacher. Participants evaluated and provided justifications for each action described. The student and teacher intentions presented were designed to identify moral, personal, and conventional domain-based concerns. Both young children and adolescents showed efforts at coordinating the various contextual elements of each situation. Results indicate that responses varied by whether the actions promoted equality or inequality, the type of act depicted in the situation, the domain of intention attributed to the student and teacher, and by the age of the participant. The changes seen in participants’ reasoning by age further contextualizes and expands prior work within Social Domain Theory and supports the central claim that children and adolescents engage in multifaceted reasoning about social situations when domain-based judgments may be in opposition. These findings also suggest that reasoning about compliance with authority dictates and deception are not endorsed or rejected universally by either young children or adolescents, but rather used as a tool to promote equality consistent with their moral judgments, and increasingly with age, as a way to maintain autonomy over actions that are not considered legitimately restricted by others, including authority figures or existing rule.

Advisor: Turiel, Elliot
Variational sampling and optimal design of rare nonequilibrium molecular dynamics

Nonequilibrium driving can independently tune the structure and dynamics of molecular and colloidal systems, resulting in novel assembly and transport phenomena that are not found in equilibrium. Exploring nonequilibrium dynamics through numerical simulations is crucial towards understanding the working mechanisms of functional inorganic or biological materials. However, behavior of dynamical observables in such systems at experimental timescales is often dominated by statistically rare fluctuations that are poorly sampled in simulated trajectories. Rare event sampling algorithms that assume a Boltzmann distribution of configurations and detailed balance for dynamics cannot be applied out of thermal equilibrium, thus limiting available techniques that can efficiently sample nonequilibrium rare events./nIn this thesis we have developed novel variational algorithms for the sampling and design of rare nonequilibrium molecular dynamics trajectories by application of an optimized driving force. This approach relies on the equivalence of a trajectory ensemble conditioned on a rare event to occur to an ensemble driven with the optimal force where the rare event occurs typically. For systems with many interacting degrees of freedom, we numerically learn the optimal force within arbitrary basis sets by statistically estimating explicit gradients of trajectory probability. This method allows us to efficiently compute large deviation functions of dynamical observables in nonequilibrium steady-states, and to automate the inverse design of self-assembling colloids and molecular machines for desired structure and dynamics. We have finally augmented our approach with reinforcement learning techniques, resulting in a new paradigm to efficiently compute nonequilibrium reaction rates, Variational Path Sampling. Our approach of using optimized forces to improve sampling of nonequilibrium trajectories is versatile and can give access to rare reactive fluctuations and dynamical phases that cannot be sampled otherwise.

Advisor: Limmer, David T.