VictorJuiz/thesis huggingface dataset:
Collaborative innovation : the antecedents, consequences, and valuation of technological resource contributions

The innovation performance of entrepreneurial ventures critically depends upon their management of internal and external relationships. Within organizational boundaries, how cofounders collectively contribute resources and efforts can significantly impact the process of technological ideation and commercialization. Beyond organizational boundaries, how entrepreneurial ventures manage their external relationships with partners, such as product users, is also vital for innovation and overall performance. Across three inter-linked papers, this dissertation investigates collaborative innovation both within and beyond the organizational boundary of entrepreneurial ventures, focusing on the antecedents, consequences, as well as valuation of technological resource contributions. Using a quasi-natural experiment, the first paper examines how innovation-driven collaborations between ventures and their product users can be undermined by legitimacy concerns stemming from another domain within the multiplex relationship between the two parties. The second paper analyzes the organizational consequences of venture-user collaborations and suggests heterogeneous effects on different types of successes (initial public offering vs. acquisitions). Finally, the third paper explores how the type of resources contributed (technological vs. non-technological) shapes initial equity share division among cofounders as well as equity dilution over time. Together, this dissertation contributes to the literature on interorganizational relationships, innovation, and entrepreneurship and provides policy implications

Keywords: Management Science and Engineering.
Advisor: Katila Riitta
Byers Thomas (Thomas H.)
Eesley Charles
Collective action, collective hopes : cross-cultural resource management in Aotearoa New Zealand's Mātaitai fishing reserves

This dissertation explores how cross-cultural resource management efforts incorporate disparate ontologies, using the mātaitai reserves (customary fishing reserves) in the Mahia Peninsula of Aotearoa New Zealand as a case study. Here, indigenous Māori and government stakeholders work together to manage these reserves in a manner that respects indigenous worldviews and encourages sustainable ocean harvesting. The potential for collective action in this context is affected by ontological assumptions, legibility practices, and the affective power of hope

Keywords: Anthropology.
Advisor: Crowder Larry B.
Durham William H.
Bauer Andrew M. 
Ormond Adreanne
Colloidal nanoparticle catalysts for sustainable energy applications

Rising greenhouse gas emissions drive climate change causing catastrophic consequences such as reduced crop yields and adverse weather disasters. Avoiding these climate outcomes requires development of technologies for both emission control, to convert pollutant gases such as carbon monoxide (CO) and hydrocarbons (HCs) into less harmful carbon dioxide (CO2), and CO2 conversion into useful products. The first part of my talk focuses on stable and active catalysts for exhaust emission control to eliminate the discharge of noxious gases from transport vehicles operating on fossil fuels. Platinum (Pt) and palladium (Pd) are excellent at combusting CO and HCs into CO2, but unable to maintain high performance under harsh conditions. Initially small PdPt nanoparticles (NPs) agglomerate into large crystallites with reduced surface area and, thus, lose their effectiveness. To prevent these metals from deactivation, I have developed a nanocasting strategy to encapsulate PdPt NPs inside alumina. Here, I embed the metal NPs inside a polymer, infiltrate the metal-polymer composite with an aluminum precursor, and calcine the resulting material to get the metal NPs distributed inside alumina. Such NPs demonstrate unprecedented stability by maintaining high rates for hydrocarbon combustion in the harshest operating environment the catalysts could be subjected to, thus surpassing state-of-the-art emission control materials. The second part of my talk is on developing catalysts for CO2 conversion into fuels and chemicals, by reacting CO2 with hydrogen. This reaction is not applied on a large-scale due to several challenges, including poor selectivity: instead of forming one desired product, the reaction forms a wide range of species, making a separation process prohibitively expensive. Overcoming this barrier requires identifying what specific sites on a catalyst surface are responsible for different products - given that the catalysts change under reaction conditions -- and designing catalysts with desirable properties. To this end, I make materials with controlled size, shape, and composition and track changes in their structure using X-ray absorption spectroscopy. Overall, my research efforts are focused on engineering catalysts for zero greenhouse gas emission energy technologies

Keywords: Chemical Engineering.
Advisor: Cargnello Matteo
Bare Simon
Jaramillo Thomas Francisco
Colloidal physics modeling of biomolecular behavior to enable cell building

Building cells from scratch requires understanding how molecules -- the building blocks of cells -- move and interact to produce behavior. To better address this challenge I apply colloidal physics -- how microscopic particles behave in liquids -- to cell biology, leveraging protein synthesis as a model process. Specifically, I develop a modeling framework for representing the physical motion and chemical reactions of individual molecules in the full context of crowded cytoplasm, showing how individual tRNA and ribosomes diffuse and interact to enable protein synthesis. Using my framework, I elucidate a putative mechanism underlying how ribosomes become more productive in faster growing cells, an experimental observation that has remained unexplained for 50 years. Specifically, I show that the nucleoid-excluded cytoplasm, where translation primarily occurs, becomes three times more packed with molecules in faster growing cells. While increased packing slows molecular movement down, molecules also become closer together, a general phenomenon that enables matching tRNA and ribosomes to find each other more quickly and thus speeds up protein synthesis. I then explore the expected properties of cytoplasm for hypothetical cells that grow faster than ever observed. I predict that further increases in crowding would provide diminishing returns for or hinder protein synthesis, suggesting a physical limit on growth rate. Finally, I extend colloidal physics modeling directly to the design of synthetic cells by calculating how the relative abundances of tRNA can be engineered to provide optimal protein synthesis strategies for both wild-type and codon-reduced genomes. Engineered tRNA abundances are predicted to enable up to ~20% faster and ~50% slower protein synthesis compared to natural abundances. Colloidal physics modeling and engineering can be expanded across cell types and processes, supporting the general design and construction of cells

Keywords: Bioengineering.
Advisor: Endy Andrew D. 
Covert Markus
Zia Roseanna
Colonial Care: Exploring How a Potential Funding Increase Could Improve Puerto Rico’s Medicaid Program and Health Care System

This thesis analyzes the potential impact of raising the Medicaid Federal Medical Assistance Percentage rate for Puerto Rico on the island’s health system. It begins with an historical overview of the Medicaid program in Puerto Rico and of problems related to program administration and funding, both of which differ substantially from Medicaid in the mainland United States. It then compares the effects of the Affordable Care Act’s Medicaid expansion in Puerto Rico to what occurred in the rest of the United States and analyzes relevant literature pertaining to the benefits of Medicaid expansion on the mainland. The use of block grant funding for Puerto Rico, as opposed to a full Medicaid expansion, resulted in the island experiencing fewer benefits than its state counterparts. Based on an original analysis of publicly reported data on healthcare utilization and health outcomes in Puerto Rico, and application of an evaluative framework adapted from other health policy analyses, the thesis concludes that the funding mechanism for Puerto Rico’s Medicaid program should be altered to match that of the rest of the United States.

Keywords: Puerto Rico
Medicaid
FMAP
Affordable Care Act
Medicaid expansion
physician retention
health care
health outcomes
Thesis
Advisor: Mello Michelle
Communication and signal processing methods for efficient optical link component utilization

The exponential growth trend of network traffic during the past few decades continues to be driven by emerging applications. As global traffic demands rise, the share of traffic carried by optical fiber is increasing simultaneously. Therefore, it is imperative that future optical fiber links take full advantage of the resources available to maximize capacity. This work aims to apply communication theory concepts and signal processing techniques to optical communication links to ultimately increase capacity in systems that are subject to component limitations. First, we explore the advantages of adaptive-rate access networks, implemented through adaptive coding and modulation that adjusts the forward error correction code rate and modulation order in response to the channel conditions of each user. Our adaptive coding and modulation scheme can achieve an equalized bit rate of 40 Gb/s with 25G-class components on a network with fiber lengths of 5 to 25 km. Next, we study the fundamental performance of Kramers-Kronig detection, a technique that uses signal processing to retrieve the phase of a signal from its intensity measurement, making comparisons to standard coherent detection and standard direct detection. Finally, we study the benefit of successive interference cancellation in frequency-selective space-division multiplexed ultra-long-haul submarine links with mode-dependent gain caused by optical amplifier limitations. We show that successive interference cancellation, in conjunction with coding across modes, decreases capacity loss resulting from mode-dependent gain by 39% at 15,000 km and increases system reach by 28% compared to minimum-mean-square error detection in a link with moderate mode-dependent gain

Keywords: Electrical Engineering.
Advisor: Kahn Joseph M. (Joseph Mardell)
Miller D. A. B.
Solgaard Olav
Companionate Marriage: A Continued Confucian Construct

The hierarchical relationship between husband and wife served as a cornerstone of Confucian society. The rise of companionate marriages— marital relationships rooted in romance during the Ming and Qing—supposedly signified the breakdown of the traditional family structure by emphasizing the conjugal couple and increasing elite women’s agency. Thus, some scholars posited that the changing ideals of female virtue driven by the concept of companionate marriage demonstrated the early decline of Confucian hegemony and the start of modernity during the late imperial era. This increase of female independence and influence also conflicted with the May Fourth’s dark depiction of Confucianism. This thesis was inspired by the academic debate over the shifts companionate marriages caused in the traditionally Confucian-prescribed family and social structures, as well as the implications these changes had on the conceptions of modernity in China. This thesis aims to add to this scholarly discourse by building upon the foundations laid by Dorothy Ko’s revisionist history, Teachers of the Inner Chambers. Women’s voices historically have been dismissed in discussions of dynastic China despite their importance in the maintenance of the traditional social order. Therefore, this thesis places women’s voices back into the academic discussion by primarily focusing on literary works authored by elite women within companionate marriages. Contrary to challenging traditional society, women’s words prove that companionate marriages perpetuated traditional Confucian constructs. As narrated by women’s works, the emotional bonds and shifting ideals of female virtue resulting from companionate marriage ideals resided within and reinforced classical values. Their pieces demonstrate the active commitment of elite women to upholding traditional proprieties, as evidenced by comparisons to traditional moral works for women. While elite women were inspired by the romantic ideals of companionate marriage depicted in fictional works such as The Peony Pavilion, they sought to balance traditional female virtues with their desire to fulfill their new roles as companionate wives. This thesis seeks to show that companionate marriages did not represent the early decline of traditional China but instead displayed the dynamic nature of Confucianism during the late imperial era.

Keywords:  Stanford Global Studies
Stanford Global Studies
East Asian Studies
China
Ming-Qing
Companionate Marriage
Gender
Confucianism
Thesis
Advisor: Zhou Yiqun
Comparative and population genomics of large carnivores

The combined effects of land-use change, habitat loss, and climate change have placed incredible stress on species living through the Anthropocene. For charismatic megafauna, especially predators, these affects are exacerbated by additional losses from poaching and human-wildlife conflict. An open question in conservation biology and genomics, concerns how these declines manifest at the genomic level, and whether we can distinguish between populations that are on the precipice of extinction, compared to those which are simply small. In addition, there are several open questions as to how we can best manage these populations into the future; should low-diversity populations be mixed to maximize adaptive potential, or should the populations be managed separately to maintain unique lineages? Can genomics tell us something about recent and historic declines? In order to answer these questions, genome-level resources for the species in question are preferred, since patterns in the genome (such as heterozygosity and the accuracy of run of homozygosity detection) rely on highly-continguous assemblies and large amounts of whole-genome resequencing data. Here, I focus on the generation of high quality genomic data for members of the order which have experienced substantial declines over the last 100 years. I generate high-quality genome assemblies from several members of the Panthera group, including the lion, the snow leopard, the African leopard, and the tiger, in addition to high-quality genomes for several African wild dogs. I examine how using different reference assemblies can lead to biased estimates of various statistics of interest, such as heterozygosity and runs of homozygosity. I then focus on the tiger, detailing the patterns of variation and demographic history in the wild tiger, with a focus on individuals from India, where a previous lack of sampling has inhibited our evolutionary inference. Together, this scholarship provides an abundance of genomic data for species where little to no data existed, investigates the biases and pitfalls of genomic data, and uses range-wide sampling of a species of conservation concern to generate management recommendations based on signals of recent demographic declines

Keywords: Biology.
Advisor: Hadly Elizabeth Anne
Petrov Dmitri Alex
Campana Michael G. 
Rosenberg Noah
Complex behavior enforced by protein-based inheritance : investigating the consequences of novel prion elements on sexual reproduction and genome integrity in the budding yeast Saccharomyces cerevisiae

Prions are proteins that can adopt multiple conformations, at least one of which is self-templating. Ensuing changes in protein activity are heritable over long biological timescales, in a non-Mendelian fashion. In budding yeast, specific prions have been identified that confer distinct phenotypes and fitness advantages in different conditions with induction and loss at rates higher than mutation. These prions can be induced by different stimuli -- in some cases, stimuli to which they provide an adapted response. This bi-stable switch behavior provides a framework for bet-hedging strategies whereby organisms can adapt to fluctuating environments in a reversible manner. Here, I review phase-separation dynamics and their biological relevance as they relate to and inform prion behavior (Chapter 1), followed by reports of two cellular states: [MIX+] (Chapter 2), and Ste+ (Chapter 3). [MIX+] is a prion state associated with the helicase, Mph1. It decreases mutation frequency, increases the incidence of recombination, and provides broad-spectrum resistance to a variety of DNA damaging conditions. Its induction can be stimulated by DNA-damaging conditions to which it provides resistance. Ste+ is a putative prion state associated with the Aurora B Kinase scaffold, Sli15. Ste+ cells demonstrate sterility, distinct histone modification, and a unique transcriptional profile. However, the sterility phenotype is in part explained by a nonsense mutation in STE7. Nevertheless, together, these efforts represent an effort to investigate mechanisms by which S. cerevisiae can modulate genomic integrity and diversity through switch-like control of sexual reproduction, mutation, and recombination

Keywords: Biology.
Advisor: Jarosz Daniel
Cyert Martha S.
Feldman Jessica L.
Skotheim Jan
Straight Aaron
Compositional reasoning in robot learning

To carry out diverse tasks in everyday human environments, future robots must generalize beyond the knowledge they are equipped with. However, despite recent advances in "end-to-end" deep learning, today's robot learning methods are still limited to specializing in one task at a time. At the same time, humans perform everyday tasks with ease. But instead of learning each task in silos, we distill reusable abstractions from our daily experiences and solve new tasks by composing known building blocks. Such compositional reasoning capability is crucial for developing future robots that are both competent and versatile. This two-part thesis presents a spectrum of techniques for building compositional generalization capabilities into robot learning systems. Part I starts by drawing prominent ideas from classical structured approaches in AI such as program induction and graphical models and design algorithms with strong representational priors. Neural Task Programming (NTP) represents task-subtask hierarchies using modular neural programs. NTP is designed to simultaneously exploit the representational capacity of neural networks for handling unstructured input and the compositional nature of programs for few-shot generalization. Similarly, Neural Task Graphs (NTGs) represent manipulation skills as nodes in a graph and leverage structures in skill preconditions for generalization. Regression Planning Network (RPN) further dissects the goal specification by grounding symbolic goals with object-centric representation and achieve zero-shot generalization to new task goals. To bring compositional reasoning closer to real-world settings, Part II of this thesis introduces our recent efforts to relax the strong representational prior assumptions: learning from unstructured video demonstrations and learning through trial-and-error. We first describe a framework for simultaneously learning complex visuomotor skills and discovering implicit compositional structures from human demonstrations. Experiment shows the approach allows a physical robot to perform long-horizon manipulation in a kitchen setup. Then we further relax the assumption of having demonstration data and enable a robot to learn compact planning representations by characterizing the pre- and post-condition of motion primitives through active interaction

Advisor: Li Fei Fei
Savarese Silvio
Bohg Jeannette
Sadigh Dorsa
Compression, generation, and inference via supervised learning

Artificial intelligence and machine learning methods have seen tremendous advances in the past decade, thanks to deep neural networks. Supervised learning methods enables neural networks to effectively approximate low-level functions of human intelligence, such as identifying an object within an image. However, many complex functions of human intelligence are difficult to solve with supervised learning directly: humans can build concise representations of the world (compression), generate works of art based on creative imaginations (generation), and infer how others will act from personal experiences (inference). In this dissertation, we focus on machine learning approaches that reduce these complex functions of human intelligence into simpler ones that can be readily solved with supervised learning and thus enabling us to leverage the developments in deep learning. This dissertation comprises of three parts, namely compression, generation, and inference. The first part discusses how we can apply supervised learning to unsupervised representation learning. We develop algorithms that can learn informative representations from large unlabeled datasets while protecting certain sensitive attributes. The second part extends these ideas to learning high-dimensional probabilistic models of unlabeled data. Combined with the insights from the first part, we introduce a generative model suitable for conditional generation under limited supervision. In the third and final part, we present two applications of supervised learning in probabilistic inference methods: (a) optimizing for efficient Bayesian inference algorithms and (b) inferring the agents' intent under complex, multi-agent environments. These contributions enable machines to overcome existing limitations of supervised learning in real-world compression, generation, and inference problems

Advisor: Ermon Stefano
Ma Tengyu
Sadigh Dorsa
Wu Jiajun (Computer scientist)
Compression of raw genomic data

With the rapid advances in genomic sequencing, the amount of genomic data being produced is growing exponentially. Several large scale sequencing projects for humans and other species are expected to further increase the volume of this data. While the initial progress was led by second generation high-throughput sequencers such as Illumina, more recently there has been increasing interest in third generation sequencers like Oxford Nanopore that enable real-time and portable sequencing of long reads. In this context, compression techniques play a crucial role in enabling efficient storage and transfer of this data. Unfortunately, the traditional general-purpose compressors like Gzip are unable to fully exploit the inherent redundancy in this data. Furthermore, in many cases the data is noisy, and it is possible to deploy lossy compression algorithms that can reduce the storage space without adverse impacts on the data quality for downstream analysis. This thesis presents two specialized compressors for genomic data, focusing on raw genomic data which consists of sequencing reads (FASTQ format) as well as raw signal data produced by nanopore sequencing (FAST5 format). We first describe SPRING, which is an efficient compressor for unaligned single and paired-end genomic reads, supporting various lossless and lossy compression modes. Next, we discuss lossy compression of nanopore raw signal data using LFZip, which is a general-purpose lossy compressor for time series and sensor data. We also discuss the evaluation of the impact of lossy compression on the performance of downstream applications like basecalling, consensus and methylation calling

Keywords: Electrical Engineering.
Advisor: Weissman Tsachy
Ji Hanlee
Wootters Mary