VictorJuiz/thesis huggingface dataset:
Twisted K-Theoretic Gromov-Witten Invariants and Euler Characteristics

We prove a twisting theorem for nodal classes in permutation-equivariant quantum K-theory, and combine it with existing theorems of Givental to obtain a twisting a theorem for general characteristic classes of the virtual tangent bundle of the moduli space of stable maps. Using this result, we develop complex cobordism-valued Gromov-Witten invariants defined via K-theory, and relate those invariants to K-theoretic ones via the quantization of suitable symplectic transformations. This procedure is a K-theoretic analogue of the quantum cobordism theory developed by Givental and Coates. Using the universality of cobordism theory, we give an example of these results in the context of ``Hirzebruch K-theory", which is the cohomology theory determined by the Hirzebruch y-genus./nWe then introduce Euler-theoretic Gromov-Witten invariants, which are based on the ordinary topological Euler characteristics of loci of curves. These invariants have useful enumerative properties. They are integer valued, there is a reliable way to remove boundary contributions, and when the moduli spaces are smooth orbifolds, have a concrete geometric intepretation. In this case, the invariants also encompass the ordinary (i.e. integer) topological Euler characteristic of the moduli space of stable maps and its compactification./nWe give a Wick-type formula computing these invariants in terms of twisted cohomological Gromov-Witten invariants, and we also show that they can be regarded as the limit as y approaches 1 of the Hirzebruch-theoretic invariants introduced earlier in this work./nSpecializing to the case of a point target space, our formulas yield similar results to the computation of the Euler characteristics of the moduli spaces of n-pointed curves, given by Bini-Harer.

Advisor: Givental, Alexander B
Nuclear Power Projects in Southeast Asia and sub-Saharan Africa – Balancing Stakeholder Perspectives and the Laws

In 2019, unprecedented extreme weather conditions occurred in several parts of the world as the temperature of the earth’s surface hit a record high. This deterioration of environmental conditions can largely be attributed to rise in emissions resulting from high energy demand. The concentration of carbon emissions in 2018 was at its highest level in over 800,000 years, at 413 parts per million driven by higher energy demand. The energy sector contributed to about two-thirds of this growth in emissions. Coal use in power alone surpassed 10 Gt CO2, mostly in Asia. Renewable energy sources and nuclear power met the majority of this growth in demand. Still, generation from coal and gas-fired power plants has increased considerably, continuing to ramp up emissions.While an effort globally is needed, developing nations are a critical part of reducing emissions. In the US and most of Europe, discussions on a cleaner grid revolve around higher reliability, more efficiency, and affordability. However, in many developing countries in Southeast Asia and sub-Saharan Africa, the bigger question is to ensure electricity supply, as the nations in those regions go through rapid industrialization and population growth. As such, these countries remain dependent on fossil fuels. In Africa for example, states grapple with power infrastructural deficit requiring over $90 billion in investment. Together, 48 countries that form sub-Saharan Africa have only 170 gigawatts (GW) in installed capacity generation. This renders approximately 625 million people in Africa without electricity. When incorporated in the grid with renewable energy sources, nuclear energy offers a workable option. Therefore, while expanding generation from renewable sources, countries in Southern Asia like India, and Southeast Asia like Thailand, Malaysia, Vietnam and the Philippines are expanding and exploring their nuclear power programs to cope with the growing energy demand while also reducing emissions. Yet, there are major barriers for these countries to pursue commercial nuclear power generation. This dissertation seeks to analyze and propose plausible options to these issues by studying the relevant international instruments and domestic policies pertaining to nuclear energy and investigate the legal and policy aspects of nuclear safety, regulation, nuclear non-proliferation, and liability management that act as barriers to nuclear energy investment in these regions.

Advisor: Biber, Eric
Forestry, Farms, Family, and Small Towns in the Hill Country of Central China

This dissertation explores the multiple processes integral to rural transformation in western Jiangxi Province, China. It examines the spatio-historical characteristics underlying the rise of small-scale private forestry, the partial mechanization of wet-rice farming, gendered translocal family reproduction, and small-town revival in this part of rural China in the early 21st century. It emphasizes the roots of and routes towards producing the hill community and small towns in the context of wider global processes. The dissertation contends that an open and relational conceptualization of space and place entails an alternative understanding of the countryside and its place within China at large. In this sense, the hill country in western Jiangxi Province is not to be understood as a ‘case’ study of a more general phenomenon. Rather, this dissertation explains the specificity of this hill region as arising not because of its ‘local’ characteristics and isolation from the outside world, but rather due to the simultaneous interconnectedness of multiple processes./nCentering on the lived experiences and everyday life of children and mothers, this dissertation recognizes alternative possibilities for the rural population of China, which maintains a remarkable degree of agency, as reflected in villagers’ aspirations and identity. Socially constructed gender relations are critical forces that affect and reflect the complexity of the countryside and the varied regional economy in today’s China./nTheoretically, the term “multiple processes” draws interconnections through the lens of “a global conjunctural frame.” This particular frame differs from the conventional approach of studying agrarian transition as a transition from peasant society to a “modern” capitalist one. Three global conjunctural moments were important in this part of rural China: the end of the Cold War; the beginning of neoliberal forms of capitalism since the late 1980s; and the post-2008 global financial crisis. One key theoretical insight of the global conjunctural frame is to denaturalize pre-given bounded units and overcome a set of dichotomies, such as global/local, agriculture/forestry, and work/everyday life. The central threads linking the four processes are history, gender, and geography.

Advisor: Hsing, You-tien
Development of a Small Unmanned Aerial System Composed of Structural Pulse Shape Discriminating Plastic Scintillators

In this work, the feasibility of utilizing a new generation of pulse shape discriminating plastic scintillators as structural materials is evaluated. The capacity to employ radiation detectors in structural roles provides potential advantages in mass-constrained and volume-constrained systems. This technology is particularly applicable to mobile-platform deployed detection payloads in which layouts can be augmented either by the addition of radiation detectors into currently incompatible placements and orientations, or through the replacement of inert structural mass by active-volume structural detector components. Additionally, the development of such materials enables the construction of payload-less radiation-detecting small unmanned aerial systems (sUASs), allowing for the miniaturization and consequent reduction of minimum source-detector measurement distances beyond currently achievable means.This work describes the design, construction, and evaluation of a payload-less radiation-mapping sUAS, originating with the development of novel PSD plastic scintillators suitable for use in the intended structural application. A comprehensive study of the PSD and mechanical properties of novel scintillator compositions, in collaboration with Lawrence Livermore National Laboratory, is described. Detector design is accomplished by simulation of complex active-volume sUAS frame geometries in GEANT4 optimizing light collection efficiency and detection efficiency, followed by experimental validation. Following computational modeling of optimal structural detector geometries, experimental active-volume sUAS frames were fabricated from which complete sUAS prototypes were constructed demonstrating semi-autonomous flight capability and onboard PSD spectroscopy. A series of prototypes was constructed, initially to prove structural feasibility in a 19 inches diameter, 2kg mass configuration, and subsequently improved upon to demonstrate PSD capability. The end result is the construction of a radiation-detecting sUAS constructed of four PSD plastic active-volume frame arms, with an overall diameter of 12 inches and approximately 1kg system dry mass. Proximity radiation mapping performance of the constructed systems was evaluated in γ-ray and neutron source localization field tests.

Advisor: Vetter, Kai
How Do Firms Mitigate Conflicts Among Creditors During Bankruptcy?

The aim of my research is to understand how firms alter their debt structure and accounting policy in response to strengthening some creditors' rights. To this end, I examine the impact of the Anti-recharacterization law, which enhances the rights of securitization creditors, on firms' debt concentration and financial reporting. By considering the conflict of interest between creditors during bankruptcy, I find that the legal reform leads firms to concentrate their debt structure (e.g., by using fewer types of debt or contracting with fewer creditors) in order to mitigate creditors' coordination costs in bankruptcy. This effect is more pronounced for firms experiencing financial distress, with low re-deployable assets, poor disclosure quality, and high accounts receivable. Additionally, firms' financial reporting becomes more conservative after the reform in an effort to compensate other creditors and improve their chances of recovering their claims to prevent coordination failure in bankruptcy. This effect is more pronounced for firms in financial distress, with high re-deployable assets, and high accounts receivable. Overall, my results highlight how firms attempt to mitigate the risk of creditors' coordination failure in bankruptcy.

Advisor: Patatoukas, Panos
Theorizing Hidden Chemical and Magnetic Order in Alloys

The popularization of concentrated solid-solution alloys has prompted a renewed search for atomic-scale chemical order among elements that appear randomly distributed within crystal lattices. However, confounding signals in electron diffraction experiments necessitate an immediate reliance on indirect measurements for detecting local order in compositionally complex alloys, as may be interpreted through computer simulations. For instance, both the structure and magnetization of the CrCoNi model system notably contradict ab initio theory for random alloys, but could be reconciled by the widespread presence of chemical short-range order. These simulations additionally find significant magnetic interactions in materials that are often assumed only paramagnetic, motivating further predictions of antiferromagnetism in binary Cr-Ni alloys, which are conventionally understood to have nonmagnetic ground states. This result indicates either the failure of standard theories or, as suggested by anomalous historical measurements, the existence of previously overlooked magnetic order that could persist well above ambient temperatures. In order to realistically model magnetic thermodynamics, a new Monte Carlo approach is developed and found to provide insight into the origin and prevalence of magnetic short-range order.

Advisor: Asta, Mark
Generative Models of Images and Neural Networks

Large-scale generative models have fueled recent progress in artificial intelligence. Armed with scaling laws that accurately predict model performance as invested compute increases, NLP has become the gold standard for all disciplines of AI. Given a new task, pre-trained generative models can either solve it zero-shot or be efficiently fine-tuned on a small amount of task-specific training examples. However, the widespread adoption of generative models has lagged in other domains---such as vision and meta-learning. In this thesis, we study ways to train improved, scalable generative models of two modalities---images and neural network parameters. We also examine how pre-trained generative models can be leveraged to tackle additional downstream tasks./nWe begin by introducing a new, powerful class of generative models---Diffusion Transformers (DiTs). We show that transformers---with one small yet critically-important modification---retain their excellent scaling properties for diffusion-based image generation and outperform convolutional neural networks that have previously dominated the area. DiT outperforms all prior generative models on the class-conditional ImageNet generation benchmark./nNext, we introduce a novel framework for learning to learn based on building generative models of a new data source---neural network checkpoints. We create datasets containing hundreds of thousands of deep learning training runs and use it to train generative models of neural network checkpoints. Given a starting parameter vector and a target loss, error or reward, loss-conditional diffusion models trained on this data can sample parameter updates that achieve a desired metric. We apply our framework to problems in vision and reinforcement learning./nFinally, we explore how pre-trained image-level generative models can be used to tackle downstream tasks in vision without requiring task-specific training data. We show that pre-trained GAN generators can be used to create an infinite data stream to train networks for the dense visual correspondence problem---without requiring any human-annotated supervision like keypoints. Networks trained on this completely GAN-generated data generalize zero-shot to real images, and they outperform previous self-supervised and keypoint-supervised approaches that train on real data.

Advisor: Efros, Alexei A
Deep Generative Models for Decision-Making and Control

Deep model-based reinforcement learning methods offer a conceptually simple approach to the decision-making and control problem: use learning for the purpose of estimating an approximate dynamics model, and offload the rest of the work to classical trajectory optimization. However, this combination has a number of empirical shortcomings, limiting the usefulness of model-based methods in practice. The dual purpose of this thesis is to study the reasons for these shortcomings and to propose solutions for the uncovered problems. We begin by generalizing the dynamics model itself, replacing the standard single-step formulation with a model that predicts over probabilistic latent horizons. The resulting model, trained with a generative reinterpretation of temporal difference learning, leads to infinite-horizon variants of the procedures central to model-based control, including the model rollout and model-based value estimation./nNext, we show that poor predictive accuracy of commonly-used deep dynamics models is a major bottleneck to effective planning, and describe how to use high-capacity sequence models to overcome this limitation. Framing reinforcement learning as sequence modeling simplifies a range of design decisions, allowing us to dispense with many of the components normally integral to reinforcement learning algorithms. However, despite their predictive accuracy, such sequence models are limited by the search algorithms in which they are embedded. As such, we demonstrate how to fold the entire trajectory optimization pipeline into the generative model itself, such that sampling from the model and planning with it become nearly identical. The culmination of this endeavor is a method that improves its planning capabilities, and not just its predictive accuracy, with more data and experience. Along the way, we highlight how inference techniques from the contemporary generative modeling toolbox, including beam search, classifier-guided sampling, and image inpainting, can be reinterpreted as viable planning strategies for reinforcement learning problems.

Advisor: Levine, Sergey
New structures on embedded contact homology and applications to low-dimensional topology

Embedded contact homology (ECH) is a powerful three-manifold invariant that encodes information about the underlying contact manifold. It has been shown that embedded contact homology is isomorphic to certain versions of Seiberg-Witten Floer homology and Heegaard Floer homology. This thesis highlights the relations of ECH to other three-dimensional Floer theories. In Chapter 2, we use ideas from periodic Floer homology, a cousin of ECH, and techniques from pseudo-Anosov maps to answer a knot detection question in knot Floer homology. This is joint work with Ethan Farber and Braeden Reinoso. In Chapter 3, we show a connected sum formula for ECH by studying pseudo-holomorphic curves in the symplectization of the contact connected sum, without going through the tremendous isomorphisms to Seiberg-Witten Floer homology or Heegaard Floer homology. Our chain level description of the connected sum complex is useful for studying studying similar formulas for other contact homologies and ECH spectral invariants in the future. In Chapter 3, we give a relative version of ECH for contact three-manifolds with convex sutured boundaries. This generalizes both sutured ECH and Lipshitz's cylindrical reformulation of Heegaard Floer homology, and provides a potential framework for bordered ECH. This is joint work with Julian Chaidez, Oliver Edtmair, Yuan Yao and Ziwen Zhao.

Advisor: Hutchings, Michael
'Holy Water’: The Crisis of White Affect, Infrastructural Threat, and Horticultural Promise in the Sacramento Valley

A network of levees, channels, and weirs means to protect Sacramento, CA from catastrophic flooding. However, civic boosters never claimed geomorphological reinvention as infrastructure’s sole fantastic promise for the capital city. Their assurances intrinsically imbricated riverine stability with guarantees of infrastructural financial steadiness and civilizational might. This economic consistency remained an alluring promise within a developmental ethos encouraging speculation and wealth concentration./nThrough racial capitalist critique, I dissect three prominent Sacramentan hydrological corporations that appealed to whiteness as aligning these values: Natomas Consolidated, the Sutter Basin Company, and the California Steam Navigation Company. Despite uneven development’s racialized landscapes alternately stockpiling potential value or sinking excessive commodities, eventually overproduction or ecological variation inexorably induces market crisis. In response, elite Sacramentans mitigated inherent capitalist contradiction by fabricating a novel white racial form tethered to infrastructural development. They imagined whiteness as a balancing force capturing nature’s potential, harnessing returns through technological abstraction, and allowing ceaseless circulation—including of capitalism’s detritus. They fantasized a universal, domestic, and fecund whiteness yielding productive landscapes from waste in whatever context. In their fantasy, whiteness could transit ceaselessly from empire’s hinterlands back to its core with minimal friction, mimicking infrastructure and avoiding expansion’s inevitable limits./nI analyze affective traces from these firms’ developmental schema to critique their reliance on whiteness across scales to resolve crisis. They prioritized the domestic imperative, technofetishism, and abstraction of whiteness through infrastructural association. The massive public works offered concrete stability for wandering investment once particular resource caches withered, individual financial parachutes for overeager speculators, and even novel intimate racial identities for an emergent imperial settler colony. I track ephemeral traces of this emotional attachment, recounting the beating heart of capitalism’s inevitable catastrophe and an innate racial recourse deferring disaster through offers of white civilizational redemption.

Advisor: Scott Lewis, Jovan
Scalable Representations for Vision and Robotics

Artificial intelligence systems have shown remarkable advancements in recent years. However, the challenge of scalability and generalization to real-world problems remains a significant issue. In this thesis, we explore the three key components of building scalable artificial intelligence systems for computer vision, including model optimizability, learning objectives, and large-scale datasets, and apply these outcomes for robotics./nOur work begins with an examination of the optimizability of vision transformers, proposing a new set of optimizability metrics and an alternative design for their patchify stem. Next, we introduce a contrastive self-supervised learning objective that reduces inductive biases in self-supervised learning, resulting in superior performance across various datasets. We then showcase the effectiveness of self-supervised visual pre-training from real-world images for learning motor control tasks from pixels, outperforming supervised baselines and matching oracle state performance./nExpanding on this, we explore self-supervised visual pre-training on images from diverse, in-the-wild videos for real-world robotic tasks, demonstrating the effectiveness of pre-trained representations across a range of tasks and embodiments. In addition, we present a sim-to-real learning-based approach for real-world humanoid locomotion using a causal Transformer, marking the first fully learning-based method for real-world full-sized humanoid locomotion. Finally, we conclude the thesis and discuss potential future directions for further research in the field.

Advisor: Darrell, Trevor
Robust Optimization of Generalized Eigenvalue Problem for Positive Semi-definite Matrices

In this paper, we propose novel algorithms for solving the worst-case robust optimization of the generalized eigenvalue problem with positive semi-definite constraint, a highly non-convex problem that has eluded traditional optimization solvers. We consider the rank-one case and the general-rank case saparately. For the rank-one case, we first present a relaxation of the KKT system, transforming the problem into a more tractable nonlinear system of equations. We then prove the tightness of the relaxation at the optimal point and introduce two algorithms for solving the relaxed system of equations. Our approach is the first to guarantee finding the global optimal solution for the problem at hand. For the general-rank case, we first solve the KKT system with one variable fixed. We then describe an algorithm searching for the variable. We then showed that our algorithm converges to a stationary point of the problem. We showcase the potential applications of our algorithms in robust adaptive beamforming and semi-supervised Fisher discriminant analysis. This work contributes significantly to the field by providing a globally optimal solution to a highly non-convex problem with broad applicability in various disciplines.

Advisor: Gu, Ming