VictorJuiz/thesis huggingface dataset:
Natural Product Discovery from Solventogenic Clostridia

Secondary metabolites, or natural products, are small molecules which frequently possess biological activities and are a critically important resource for the development of new pharmaceuticals, pigments, and agrochemicals. The need for new natural product sources has led to the genomics-driven identification of the anaerobes as a promising and relatively untapped reservoir of natural product diversity. Soil isolates such as the industrially significant solventogenic clostridia are obligate anaerobes which can ferment carbohydrates to produce acetone and butanol. These bacteria are an especially promising subset of the anaerobes for discovery of natural products. This work begins with a description of the limited repertoire of known natural products isolated from anaerobes, showcasing their chemical diversity and broad range of bioactivity. Then, we describe genome mining efforts in different solventogenic clostridia. In Clostridium saccharoperbutylacetonicum, we describe genome and transcriptome profiling of secondary metabolite biosynthetic gene clusters (BGCs) to enable in-depth characterization of a nonribosomal peptide synthetase conserved in other solventogenic clostridia. This led to the discovery of a novel compound, an N-acyl dipeptidyl alcohol. We further discuss the association of the biosynthetic gene with butanol tolerance, a phenotype with industrial significance. In Clostridium roseum, we describe methods for gene delivery to enable targeting of BGCs for knockout and characterization. The products of one BGC are reported, a novel family of unusual solvatochromic compounds named the clostyrylpyrones. The biosynthesis of these compounds is proposed. In a broader survey of secondary metabolism in clostridia, we describe efforts in a panel of ten species of clostridia to identify antimicrobial potential using a traditional disc-diffusion approach. We also describe, for two of the strains, implementation of a high-throughput media screening study to identify BGC-specific chemical inducers of secondary metabolism. Overall, this work expands the known chemical diversity of natural products derived from anaerobes, supports the value of targeting these bacteria for genome mining, and extends our understanding of secondary metabolism in solventogenic clostridia.

Advisor: Zhang, Wenjun
What Supervision Scales? Practical Learning Through Interaction

To have an agent learn useful behaviors, we must be able to specify what are the desired outcomes. This supervision can come in many forms, like the reward in Reinforcement Learning (RL), the target state-action pairs in Imitation Learning, or the dynamics model in motion-planning. Each form of supervision needs to be evaluated along three cost dimensions that dictate how well it scales: how much domain knowledge is required to provide that supervision form, what is the total volume of interaction under such supervision needed to learn a task, and how does this amount increases for every new task that the agent has to learn. For example, guiding rewards provided at every time-step might speed up an RL algorithm, but it is hard to design such dense rewards that are easy-to-provide and induce a solution to the task at hand, and the design process must be repeated for every new task; On the other hand, a completion signal is a weaker form of supervision because non-expert users can specify the objective to many tasks in this way, but unfortunately standard RL algorithms struggle with such sparse rewards. In the first part of this dissertation we study how overcome this limitation by means of learning hierarchies over re-usable skills. In the second part of this dissertation, we extend the scope to explicitly minimize the supervision needed to learn distributions of tasks. This paradigm shifts the focus away from the complexity of learning a single task, hence paving the way towards more general agents that efficiently learn from multiple tasks. To achieve this objective, we propose two automatic curriculum generation methods. In the third part of the dissertation, we investigate how to leverage different kinds of partial experts as supervision. First we propose a method that does not require any reward, and is still able to largely surpass the performance of the demonstrator in goal-reaching tasks. This allows to leverage sub-optimal “experts", hence lowering the cost of the provided supervision. Finally we explore how to exploit a rough description of a task and an “expert" able to operate in only parts of the state-space. This is a common setting in robotic applications where the model provided by the manufacturer allows to execute efficient motion-planning as long as there’s no contacts or perception errors, but fails to complete the last contact-rich part of the task, like inserting a key. These are all key pieces to provide supervision that scales to generate robotic behavior for practical tasks.

Advisor: Abbeel, Pieter
Forging Royal Bonds: Dynastic Logic, Reason of State, and Marriage Diplomacy under the Spanish Habsburgs, 1526-1660

While the political preponderance of the Habsburgs stemmed from the negotiation of marriages to extend their influence and perpetuate their power, the process by which policy was constructed and implemented was far from straightforward. Over the course of successive generations, the Spanish branch of the family undertook sustained efforts to modernize the practice of marriage diplomacy, identifying distinct objectives and altering its strategic approach in response to the rapidly changing international landscape. Drawing from a wide collection of archival and printed sources in Spanish, French, and English, this dissertation shows how deliberations over the viability of proposed matches brought idealist modes of thinking rooted in dynastic logic into conversation with the emerging notion of reason of state. Focusing specifically on negotiations between Spain and two of its rivals, France and England from 1526-1660, it argues that the discursive interaction between various actors sheds light on important questions about the nature of monarchical authority, the function of familial networks, the influence of royal women, and the ultimate aim of a prudent dynastic policy.

Advisor: Dandelet, Thomas
Special Education Teacher Credential Candidates’ Promotion of Self-Determination

The purpose of this dissertation is to examine the perceptions, experiences, and applications of special education teacher candidates’ promotion of self-determination for students who have an Individualized Education Program (IEP). Self-Determination Theory (Deci & Ryan, 1985), a functional model of self-determination (Wehmeyer, 1999), Casual Agency Theory (Wehmeyer & Shogren, 2016), and the Ecological Theory of Self-Determination (Abery & Stancliffe, 2003) are the basis for the conceptual framework for the present study. A mixed-methods approach employed qualitative and quantitative methods, including surveys, document review, interviews, and observations was implemented among eight subjects. The results demonstrated that all participants place a high level of importance on the promotion of self-determination for their students; however, participant beliefs about and applications of self-determination are misaligned. Participants believed they are promoting self-determination at higher rates than were recorded during classroom observations. Participants report many variables that prohibit them from actualizing their beliefs about self-determination, such as a high-stress, high-demand, high-turnover career path.

Advisor: Cunningham, Anne E.
Kusckar Mitsch, Maryssa
Real-World Robotic Perception and Control Using Synthetic Data

Modern deep learning techniques are data-hungry, which presents a problem in robotics because real-world robotic data is difficult to collect. Simulated data is cheap and scalable, but jumping the "reality gap" to use simulated data for real-world tasks is challenging./nIn this thesis, we discuss using synthetic data to learn visual models that allow robots to perform manipulation tasks in the real world. We begin by discussing domain randomization, a technique for bridging the reality gap by massively randomizing the visual properties of the simulator. We demonstrate that, using domain randomization, synthetic data alone can be used to train a deep neural network to localize objects accurately enough for a robot to grasp them in the real world. The remainder of the thesis discusses extensions of this approach to a broader range of objects and scenes. First, we introduce a data generation pipeline inspired by the success of domain randomization for visual data that creates millions of unrealistic procedurally generated random objects, removing the assumption that 3D models of the objects are present at training time. Second, we reformulate the problem from pose prediction to grasp prediction and introduce a generative model architecture that learns a distribution over grasps, allowing our models to handle pose ambiguity and grasp a wide range of objects with a single neural network. Third, we introduce an attention mechanism for 3-dimensional data. We demonstrate that this attention mechanism can be used to perform higher fidelity neural rendering, and that models learned this way can be fine-tuned to perform accurate pose estimation when the camera intrinsics are unknown at training time.We conclude by surveying recent applications and extensions of domain randomization in the literature and suggesting several promising directions for research in sim-to-real transfer for robotics.

Advisor: Abbeel, Pieter
High-Throughput Technologies for Genome Interrogation and Editing

Microbial organisms are key drivers in processes related to human health, industrial biotechnology, and environmental ecology. In our attempts to predict, control, and design biological outcomes in each of these application areas, we require both an understanding of how microbes encode and perform their innate functions and the tools to implement engineering decisions. Here we present new technologies for interrogating genome function and editing genomes to carry out user-defined functions. First, we describe the development of CRISPRi as a platform for high-throughput genome annotation, focusing on a proof-of-concept in Escherichia coli to demonstrate our ability to map the genotype-phenotype-function landscape and uncover new design considerations for improving CRISPRi-based genetic screens. Next, we present the creation of a platform strain of Escherichia coli that achieves high rates of multiplex genome editing while retaining a low background mutagenesis rate, a favorable tradeoff that many editing strains have struggled to achieve. Finally, we report the design of novel CRISPR architectures that act not only as promising scaffolds for effector fusions but can also be programmed to sense and respond to endogenous cellular signals. Together, these technologies improve our ability to rapidly understand and engineer microbial behaviors.

Advisor: Arkin, Adam P
Political Deficits: The Dawn of Neoliberal Rationality and the Eclipse of Critical Theory

This dissertation examines the changing relationship between social science, economic governance, and political imagination over the past century. It specifically focuses on neoliberal, ordoliberal and neo-Marxist visions of politics and rationality from the interwar period to the recent Eurocrisis. Beginning with the Methodenstreit (or “methodological dispute”) between Gustav von Schmoller and Carl Menger and the subsequent “socialist calculation debate” about markets and planning, the dissertation charts the political and epistemological formation of the Austrian School (e.g., Ludwig von Mises, Friedrich A. von Hayek), the Freiburg School (e.g., Walter Eucken, Wilhelm Röpke, Alexander Rüstow), the Chicago School (e.g., Henry Simons, Milton Friedman, Gary Becker), and the Frankfurt School (e.g., Max Horkheimer, Theodor W. Adorno, Jürgen Habermas). Combining archival research, textual interpretation, and theoretical reflection on these schools, it shows how critical theorists and political economists battled over the future of capitalism and socialism by redefining state, economy, and subjectivity in terms of their (ir)rationality. It also demonstrates the significance of the Austrian, Freiburg, Chicago and Frankfurt Schools’ free appropriation of Max Weber’s binary typologies, including markets vs. planning, formal calculation vs. substantive values, and rationality vs. irrationality. In turn, the dissertation argues that these and related approaches to political and economic rationalization displaced more radical visions of the political as collective struggle and self-rule—with profound implications for the “anti-political” crises of democracy today.

Advisor: Brown, Wendy
Towards Secure Computation with Optimal Complexity

Secure computation enables a set of mutually distrustful parties to collaboratively compute a public function over their private data without leaking anything apart from the output. In this thesis, we present improvements towards obtaining secure computation with optimal computation, communication, and round complexity, in both theory and practice./n\textbf{In Theory.} We introduce a novel primitive called laconic oblivious transfer (or laconic OT for short), which allows an OT receiver to commit to a large input $D$ (of length $M$) via a short message. Subsequently, a single short message from an OT sender allows the receiver to learn $m_D[L]$, where the messages $m_0, m_1$ and the location $L \in [M]$ are dynamically chosen by the sender. We present a construction of this primitive based on the Decisional Diffie-Hellman (DDH) assumption, which makes is an innovative use of somewhere statistically binding (SSB) hashing in conjunction with hash proof systems./nSince its introduction, laconic OT has proved to be an extremely powerful tool towards achieving optimal computation and communication complexity in secure computation and beyond. In this thesis, we show a few of its very first applications including non-interactive secure computation and multi-hop homomorphic encryption for RAM programs./n\textbf{In Practice.} As a specific application of secure computation, we introduce a new notion called password-based threshold token authentication, which protects password-based authentication against single point of failures. Specifically, we distribute the role of a single server among $n$ servers and allow any $t$ servers to collectively verify clients' passwords and generate tokens, while no $t - 1$ servers can forge a valid token or mount offline dictionary attacks. We then introduce PASTA, a general framework wherein clients can sign on using a two-round (optimal) protocol that meets our strong security guarantees./nOur experiments show that the overhead of PASTA, compared to a na\"ive single-server solution, is extremely low (1-5\%) in the most likely setting where parties communicate over the internet. The overhead is higher for certain MAC tokens over a LAN (though still only a few milliseconds) due to inherent public-key operations in PASTA. We show, however, that public-key operations are necessary by proving a symmetric-key-only solution impossible.

Advisor: Garg, Sanjam
Out of Order: Radical Lawyers and Social Movements in the Cold War

My dissertation, Out of Order: Radical Lawyers and Social Movements in the Cold War, analyzes how progressive lawyers coordinated the legal defense for organizations and activists, while at the same time defining and practicing their own politics and expectations. Through the lens of a particular organization, the National Lawyers Guild, I examine the trajectories of the labor movement, the struggle for social justice, and expressions of international solidarity, and show how these trajectories diverged and intersected throughout the second half of the twentieth century. I argue that the NLG served a significant function. It was an organizing space where lawyers and activists would discuss and develop legal strategies and political tactics and test the limitations of legal — and extra-legal — protest. Consequently, the NLG became a mirror that helped make and, in turn, reflected the political coalitions and ideological ruptures within the Left during this often tumultuous era. The variety of progressive and radical positions debated, as well as the numerous organizations that worked with and around the NLG, gave the organization a unique vantage point and a continuous ancillary role in the trajectory of social movements./nThe global incorporation of a human rights platform during this period captured a significant measure of the energy and attention of radical lawyers. This development helped broaden the scope of civil and economic rights struggles. The continuing impact of this development on the ideological and practical construction of progressive litigation, community empowerment, and networks of solidarity during the late Cold War has been profound. In juxtaposition to the experience of lawyers in the United States, the final chapter looks at progressive attorneys in Mexico. This chapter analyzes the role lawyers played in regards to social movements in other legal systems and the broader impact human rights discourse had on domestic legal strategies in Mexico in particular. Critically, it stresses the international undercurrents of radicalism and social justice between the 1960s and 1980s.

Advisor: Martin, Waldo E
Mean action of periodic orbits of area-preserving annulus dieomorphisms

An area-preserving diffeomorphism of an annulus has an "action function" which measures how the diffeomorphism distorts curves. The average value of the action function over the annulus is known as the Calabi invariant of the diffeomorphism, while the average value of the action function over a periodic orbit of the diffeomorphism is the mean action of the orbit. If an area-preserving annulus diffeomorphism is a rotation near the boundary, and if its Calabi invariant is less than the maximum boundary value of the action function, then we show that the infimum of the mean action over all periodic orbits of the diffeomorphism is less than or equal to its Calabi invariant.

Advisor: Hutchings, Michael
Applications of Magnetic Particle Imaging

Magnetic particle imaging (MPI) is an emerging tracer imaging modality with high sensitivity and ideal image contrast. MPI uses low-frequency magnetic fields to image the spatial distribution of superparamagnetic iron oxide (SPIO) tracers. There is ideal image contrast because background tissue (bone, muscle, blood, fat) produces no MPI signal. Moreover, there is zero depth attenuation of low-frequency magnetic fields in tissue, allowing for quantitative imaging. In this dissertation, I describe several new preclinical imaging applications which take advantage of the unique physics of MPI: lung perfusion imaging, white blood cell tracking, and enzyme-responsive nanocarriers./nPulmonary embolism (PE), a blood clot in the lung, is usually diagnosed with CT pulmonary angiography. However, patients with poor renal function are not able to tolerate the high iodine dose. The MPI tracer is kidney-safe because it clears through the liver and spleen instead of the kidneys. Moreover, imaging around air-tissue interfaces such as those in the lung do not result in imaging artifacts (unlike in magnetic resonance imaging) because of the comparably low gradient homogeneity needed. Hence, MPI has ideal properties for a kidney-safe alternative lung perfusion imaging method. In Chapter 2, I show fabrication and optimization of the first MPI lung perfusion imaging agent, MAA-SPIO, and \textit{in vivo} lung perfusion images in a rat. I quantitatively track the biodistribution and clearance of the tracer over time. Additionally, I show that the lung perfusion imaging method can be paired with a method for lung ventilation imaging using aerosolized SPIOs. This allows for imaging of both the lung capillaries and lung airways./nIn Chapter 3, I discuss MPI white blood cell imaging. This technique is of particular interest for tracking autologous cell-based immunotherapies, such as chimeric antigen receptor T cells (CAR-T). Moreover, the natural homing abilities of the WBCs can localize difficult-to-find infections, such as osteomyelitis, and a similar technique is used in nuclear medicine. However, MPI allows for effective long-term cell tracking because no radionuclide tag is used. I demonstrate dynamic imaging of MPI tracer-tagged white blood cells (WBC) administered to \textit{in vivo} mice, and initial work on tracking these WBCs in a mouse model of inflammation./nIn Chapter 4, I demonstrate proof-of-concept work on a concept for enzyme-responsive nanocarriers. Tracers that can visualize and respond to the function of biological and cellular processes would allow for more specific disease diagnoses. I show that aggregated SPIOs have a quenched MPI signal as compared to stably-dispersed SPIOs, and that SPIOs can be encapsulated in a liposomal formulation. In the platform described, the enzyme hydrolyzes the SPIO-containing liposomes, and MPI signal quenching is observed. These projects represent novel work in diverse categories of MPI applications research, showcasing the strengths of the unique physics of MPI.

Advisor: Conolly, Steven M
Stochastic Local Search and the Lovasz Local Lemma

Stochastic Local Search and the Lovasz Local Lemma/nby/nFotios Iliopoulos/nDoctor of Philosophy in Computer Science/nUniversity of California, Berkeley/nProfessor Alistair Sinclair, Chair/nThis thesis studies randomized local search algorithms for finding solutions of constraint satisfaction problems inspired by and extending the Lovasz Local Lemma (LLL)./nThe LLL is a powerful probabilistic tool for establishing the existence of objects satisfying certain properties (constraints). As a probability statement it asserts that, given a family of “bad” events, if each bad event is individually not very likely and independent of all but a small number of other bad events, then the probability of avoiding all bad events is strictly positive. In a celebrated breakthrough, Moser and Tardos made the LLL constructive for any product probability measure over explicitly presented variables. Specifically, they proved that whenever the LLL condition holds, their Resample algorithm, which repeatedly selects any occurring bad event and resamples all its variables according to the measure, quickly converges to an object with desired properties./nIn this dissertation we present a framework that extends the work of Moser and Tardos and can be used to analyze arbitrary, possibly complex, focused local search algorithms, i.e., search algorithms whose process for addressing violated constraints, while local, is more sophisticated than obliviously resampling their variables independently of the current configuration. We give several applications of this framework, notably a new vertex coloring algorithm for graphs with sparse vertex neighborhoods that uses a number of colors that matches the algorithmic barrier for random graphs, and polynomial time algorithms for the celebrated (non-constructive) results of Kahn for the Goldberg-Seymour and List-Edge-Coloring Conjectures./nFinally, we introduce a generalization of Kolmogorov’s notion of commutative algorithms, cast as matrix commutativity, and show that their output distribution approximates the so-called “LLL-distribution”, i.e., the distribution obtained by conditioning on avoiding all bad events. This fact allows us to consider questions such as the number of possible distinct final states and the probability that certain portions of the state space are visited by a local search algorithm, extending existing results for the Moser-Tardos algorithm to commutative algorithms.

Advisor: Sinclair, Alistair