VictorJuiz/thesis huggingface dataset:
IMPLICATIONS AND COST OPTIMIZATION OF HYDROGEN INFRASTRUCTURE BUILD-OUT IN CALIFORNIA FOR HEAVY-DUTY TRUCKING APPLICATIONS

We study the impacts of hydrogen (H2) infrastructure build-out to support the heavy-duty trucking industry in California as the state strives to reach zero-emission vehicle policy targets for emission reductions. We first review the current H2 production, transportation, refueling station, and vehicle technologies used today to foster a hydrogen economy and the technologies in the research and development phase that can accelerate the transition in the future. We then determine the energy and infrastructure impacts of a complete switch of heavy-duty duty vehicles (HDVs) (greater than 8,500 lbs) in California to fuel cell electric (FCEV) from gasoline and diesel fuel sources. Results show that about a 23% increase in natural gas consumption in the state would be required to transition HDVs in the state to FCEVs with steam methane reforming with carbon capture (SMR-CCS) hydrogen production. Alternatively, if the H2 is produced through electrolysis, a 50% increase in electricity generation for the state would be required. If the electricity input for electrolysis is only from installed solar photovoltaic, over four times more capacity than what is currently installed in-state would need to be dedicated to hydrogen production. Also, over two times as much hydrogen storage volume would be needed at H2 refueling stations in comparison to typical gasoline or diesel storage tanks at current gas stations. We next assess the optimal placement of hydrogen production facilities throughout the state to minimize the cost of H2 infrastructure to support HDVs. Qualitative results show that placing SMR-CCS production facilities in Northern California and electrolysis production facilities in Southern California is optimal. Through technoeconomic modeling efforts, we find that lower energy input costs, and larger production facility size yield most cost-effective hydrogen production, distribution, and dispensing costs with SMR-CCS being about $5/kg H2 less than electrolysis hydrogen production in Sacramento, California. Our future work will expand the technoeconomic model to include production costs in each county of California and will account for technology cost reductions as the hydrogen economy grows and technologies mature.

Keywords:  Department of Energy Resources Engineering
Hydrogen
California
Heavy-duty vehicles
Steam Methane Reforming
Electrolysis
Text
Thesis
Advisor: Benson Sally
Improve entrepreneurial funding screening and evaluation : business success prediction with machine learning

As entrepreneurial funding supports the growth of entrepreneurial firms, it can be viewed as the fuel that enhances the creation, development, and growth of new technologies, industries, and markets. However, investing in entrepreneurial firms is highly risky. For example, Venture Capitalists (VCs) may receive a large number of business plans/proposals every year but only a few of them can be successful. Since VCs typically employ a small number of people, they do require a more effective and efficient screening and evaluation process. Prior research has mainly focused on identifying evaluation criteria to help VCs predict the business success of these firms. Nevertheless, relying on VCs' self-reporting and small regional datasets, these earlier studies have little agreement on the evaluation criteria. Therefore, it is difficult to employ those criteria to predict business success in practice. In this work, we propose data-driven approaches using machine learning methods as a complementary methodology to help VCs predict companies' success when they screen and evaluate investment deals. We start by verifying new evaluation criteria with large datasets and then focus on applying machine learning methods to predict business success. We compare different machine learning methods and discuss how VCs can benefit from the prediction. We also apply deep neural networks and few-shot learning methods to two challenging scenarios faced by the VCs: (1) when the companies are just founded and don't have any funding history; (2) when the companies are from an emerging industry that doesn't have a lot of historical data to learn from

Keywords: Management Science and Engineering.
Advisor: Tse Edison
Eesley Charles
Ye Yinyu
Improving and accelerating particle-based probabilistic inference

Probabilistic inference is a powerful approach for reasoning under uncertainty that goes beyond point estimation of model parameters to full estimation of the posterior distribution. However, approximating intractable posterior distributions and estimating expectations involving high-dimensional integrals pose algorithmic and computational challenges, especially for large-scale datasets. Two main approaches are sampling-based approaches, such as Markov Chain Monte Carlo (MCMC) and Particle Filters, and optimization-based approaches, like Variational Inference. This thesis presents research on improving and accelerating particle-based probabilistic inference in the areas of MCMC, Particle Filters, Particle-Based Variational Inference, and discrete graphical models. First, we present Sample Adaptive MCMC, a particle-based adaptive MCMC algorithm. We demonstrate how Sample Adaptive MCMC does not require any tuning of the proposal distribution, potentially automating the sampling procedure, and employs global proposals, potentially leading to large speedups over existing MCMC methods. Second, we present a pathwise derivative estimator for Particle Filters including the resampling step. The problem preventing a fully differentiable Particle Filter is the non-differentiability of the discrete particle resampling step. The key idea of our proposed method is to reformulate the Particle Filter algorithm in such a way that eliminates the discrete particle resampling step and makes the reformulated Particle Filter completely continuous and fully differentiable. Third, we propose stochastic variance reduction and quasi-Newton methods for Particle-Based Variational Inference. The insight of our work is that for accurate posterior inference, highly accurate solutions to the Particle-Based Variational Inference optimization problem are needed, so we leverage ideas from large-scale optimization. Lastly, we introduce a meta-algorithm for probabilistic inference in discrete graphical models based on random projections. The key idea is to run approximate inference algorithms for an exponentially large number of samples obtained by random projections. The number of samples used controls the trade-off between the accuracy of the approximate inference algorithm and the variance of the estimator

Advisor: Bohg Jeannette
Lai T. L.
Ma Tengyu
Improving care delivery in integrated health care systems

In this dissertation, I leverage datasets drawn from administrative and clinical records of two large integrated health care systems to study the management of chronic health conditions at the population level. I use a combination of simulation modeling and econometric methods to provide insights around clinical decision-making and care delivery improvement, as well as contemporary evidence of screening effectiveness. The first two chapters focus on the treatment of opioid use disorder (OUD) within the Veterans Health Administration (VA). In Chapter 1, I develop a simulation model of Veterans seen for OUD and use calibrated model parameters to characterize treatment durations for which elevated mortality risks during transitions into and out of treatment are balanced by mortality reductions during stable treatment. Chapter 2 extends this modeling analysis to inform the prioritization of efforts to improve care and reduce opioid-related and all-cause mortality among Veterans with OUD. It projects future diagnosis and treatment trends in the absence of additional system intervention by the VA, as well as the effects of hypothetical policies to increase treatment uptake. Chapter 3 takes a retrospective approach to assess the effectiveness of preventive screening in population health management. Using an instrumental variables approach, I evaluate the clinical outcomes of a screening program for abdominal aortic aneurysms (AAA) implemented by a large integrated healthcare system in Northern California

Advisor: Owens Douglas K.
Goldhaber-Fiebert Jeremy D.
Salomon Joshua A.
Improving neural language models with black-box analysis and generalization through memorization

Neural language models (LMs) have become the workhorse of most natural language processing tasks and systems today. Yet, they are not perfect, and the two most important challenges in improving them further are (1) their lack of interpretability, and (2) their inability to generalize consistently, both in- and out-of-distribution. In this dissertation, I first describe my work on studying these LMs via black-box analysis, in order to understand how their predictions change in response to strategic changes in inputs. This makes model predictions more transparent by highlighting the features of the input that the model relies on. Then, I describe my work on Generalization through Memorization -- exploiting the notion of similarity between examples by using data saved in an external memory and retrieving nearest neighbors from it. This approach improves existing LM and machine translation models in terms of both in- and out-of-domain generalization, without any added training costs. Beyond improving generalization, memorization also makes model predictions more interpretable

Advisor: Jurafsky Dan
Liang Percy
Manning Christopher D.
Improving prenatal care through liquid biopsies

Pregnancy complications like preeclampsia and preterm birth pose huge risks to fetal and maternal long-term health and a large financial burden. They also disproportionately affect people of color who frequently interact with a healthcare system that turns away from and ignores them. Adding to this stark reality, we cannot predict either condition early enough to allow for meaningful clinical intervention. To monitor prenatal health and diagnose pregnancy complications, we have developed 3 liquid biopsy tests that measure cell-free RNA (cfRNA) to predict how far along a pregnancy is and whether a mother is at risk of preeclampsia and preterm delivery long before traditional diagnostics. This work paves the way for affordable, simple, and reliable tests for preeclampsia and preterm delivery -- risks that no other test can presently diagnose early enough to allow for meaningful clinical intervention. We further demonstrate that cfRNA changes reflect the multifactorial nature of preeclampsia and provide a means to non-invasively monitor maternal organ health. To enable such discoveries, we have also built a low-cost, semi-automated pipeline to process samples, a notoriously tedious task, which to date, has extracted cfRNA from nearly 1100 samples in 9 days. This pipeline is generalizable for many nucleic acid extraction applications, thereby helping to democratize large-scale discovery stage work and presenting a means for smaller labs to process large sample sets with higher technical reproducibility. Altogether, we have built tools to understand pregnancy and its complications, and shown that cfRNA molecules present in blood plasma are a rich data source with the potential to change the standard of care and empower mothers to more strongly advocate for themselves during pregnancy

Keywords: Bioengineering.
Advisor: Quake Stephen Ronald
Nuyujukian Paul Herag
Stevenson David A.
Improving the Policing of Mental Health in San Jose: Rethinking How Police Respond to Mental Health Crisis

Prompted by the mass global protests during the summer of 2020 around racial equity and police brutality, cities around the nation are reexamining the role that police officers play in our daily lives. As part of the broader initiative around police reform, many cities are exploring opportunities for police alternatives, specifically during calls for service. More specifically, cities and states have begun to examine the complex role that police officers play during one of the most common calls for service: a mental health crisis.
Since the introduction of San Jose Mayor Sam Liccardo’s nine-point plan to reform the San Jose Police Department (SJPD) through “reimagining policing by disentangling police roles” in May 2020, increased support has been provided to specifically reimagine the role of police in mental health crises. A number of initiatives that aim to improve the police-mental health response have already been implemented in San Jose. Initiatives already in place in San Jose include mandating the introduction of Crisis Intervention Training as well as the roll-out of an SJPD specific Mobile Crisis Response Team (MCRT), details of which are expanded on in this paper.
The research presented in this paper focuses on first understanding the current role of the SJPD in mental health emergency response. Through this background research we identified three key stages of the mental health emergency workflow: (1) In-Bound Calls - the point from which the reporting person calls emergency services up until the dispatch of units on the scene (2) Units on Scene - the point at which emergency units arrive at the scene, up until the point at which a 5150 hold is deemed to be, or not be necessary, and (3) Post-Response - all events after the units leaves the scene.
Our research examines the effectiveness of current SJPD initiatives across these three stages. Using both quantitative and qualitative methodologies, it then identifies best practices of police- mental health response for the SJPD to consider as key areas of opportunity.

Keywords:  Public Policy Program
Mental Health
San Jose
Policing
Practicum Report
Stanford University
Humanities and Sciences
Public Policy Program
Thesis
Advisor: Nation Joe
Pal Chee Christine
Stroud Mary
Improvising places. On evaluating the complexity of open spaces in neolithic Çatalhöyük

This dissertation investigates the use and development of open spaces in Neolithic settlements in the context of the social shifts and regional trends noted in West Asia. Investigations of open space in the scholarship are varied and articulate with broader theoretical and methodological discussions such as the relationship between architectural form and social organization, the identification, role, and scale of ritual within Neolithic communities, and the development of incipient social hierarchy or the exploration of social heterarchy. While this ground may already seem well-trodden, the disproportionate focus on Neolithic structures as the basic social and productive units of analysis has left some research avenues unexplored. Conceptually, open spaces occupy a liminal zone between the inside (of the house) and the outside (of the settlement). Despite their archaeologically elusive nature, they represent the common physical and social setting for the site's inhabitants and thus hold untapped analytical potential for elucidating communal life in the Neolithic. My project uses as a case study the high-resolution excavation of a multiphase open space and the systematic and multifaceted re-evaluation of open spaces at the Neolithic tell site of Çatalhöyük (7,100-6,000 cal. BC), Turkey. One of the main arguments of this dissertation is that integrating these spaces into the Neolithic social and architectural landscape moves us away from calcified conceptualizations of open spaces as tabulae rasae and instead requires us to frame their development within more dynamic and frameworks than the public-private continuum. The chronological depth of the data at Çatalhöyük allowed me to juxtapose everyday spatial practices with longer-term social processes—highlighting the interplay between them. Thus, there are two distinct issues that this project addresses: 1. The production of nuanced excavation data to better understand open space use and development on a granular scale, and 2. A systematic re-evaluation of open spaces at Çatalhöyük in general and their re-integration into the settlement's social and architectural landscape⁠ through the collation and analysis of several strands of data—moving us towards a more comprehensive and powerful narrative of their overall role in settlement and society

Keywords: Anthropology.
Advisor: Hodder Ian
Banning E. B. (Edward Bruce)
Bauer Andrew M. 
Voss Barbara L.
In Absence and from Transference: Adoption, Abandonment, and Uneasy Familial Bonds in the Chinese Tradition

Since the People’s Republic of China opened for international adoption in the early 1990s, a great deal of scholarship has been produced regarding the One-Child Policy and overseas Chinese adoptees. Rather than looking at the contemporary world, this project is concerned with the topics of adoption and child abandonment in premodern Chinese culture and society. It asks: what did it mean to be an adoptee or “rejected” child in “traditional” China? To understand the realities of these two distinct (though at times overlapping) groups, I examine how obtaining and dismissing descendants functioned as a custom intended to protect the family as a unit. Further, the conceptual, ethical, gendered, and symbolic dimensions of these practices are explored: male adoptees occupied an ambiguous and tenuous position in their families, society, and Confucian orthodoxy, while stories of abandoned children were utilized as cultural tropes and for moral instruction. This sociocultural and historical study is also accompanied by an analysis of the development of welfare institutions and programs for needy populations during Ming-Qing China. Created in response to heightened attention on infanticide (particularly female infanticide) as well as specific crises, child relief efforts changed adoption procedures, gave rise to novel "adoptive" relationships, and presented opportunities for domestic and foreign benefactors to impose their own values onto family-less Chinese children.

Keywords:  Stanford Global Studies
Stanford Global Studies
East Asian Studies
Asia
China
Abandoned children
Adoption
Adoptees > Family relationships
Adopted children
Adoptive parents
Foundlings
Foundlings > Care
Foundlings > History
Foundlings in literature
Text
Thesis
Advisor: Zhou Yiqun
In Search of the Present: Narrative Time in 'To the Lighthouse'

The objective of this thesis is to explore the intricacies of narrative time in Virginia Woolf’s To the Lighthouse. Philosophers and literary theorists of the last century have thoroughly mined this novel, identifying passages that seem to indicate that Woolf agreed with Bergson, or Russell, or McTaggart, or Heidegger. These theorists have investigated Woolf’s writings and bibliography with a fine-toothed comb, searching for evidence that would give them license to triumphantly consider the matter settled. I plan to take a different approach.
I begin by investigating the work of those that came before me, laying a philosophical foundation from which to begin my analysis. I consider the works of Paul Ricœur, Ann Banfield, Martin Hägglund, and Paul Sheehan in relation to time in To the Lighthouse.
I then follow a few images that, when juxtaposed, have particular pertinence to time: gardens and leaves, and knots and knitting. These images are powerful articulations of non-linear time and speak to the compression of time within the novel. The recurrence of these images condense the narrative discourse, shaping the reader’s understanding of time and duration. While characters throughout the book progress non-chronologically and are thrust back into their memories, the reader reenacts this process as she experiences recursive imagery, thus pulling the past into the present.
Finally, I turn to the role of the lighthouse as a mediator of the present. While water in the text calls to mind the terror of eternity and unmediated time, the lighthouse is an important interruption of the infinitude of the horizon. In some ways its beam is a faithful maestro of Chronos, marking the inevitable succession of nights following days. However, its main role in the text is to extend the present, thus staving off the dread of the future. I consider the strong identification between Mrs. Ramsay and the lighthouse’s beam, concluding that while Mrs. Ramsay plays a similar role of circumscribing the present, her mortality allows us glimpses of how characters might mediate their own time. Ultimately, the lighthouse interrupts the isolated gravity of the past and boundless ocean of the future–steering characters into a shared present.
I conclude that while narrative time is certainly nonlinear throughout the text, the phenomenological and chronological are inextricable. Delineations of time are portrayed as critical: even though the past often resurfaces, it must be intentionally woven into the present.

Keywords:  Program in Comparative Literature
comparative literature
modernism
virginia woolf
narrative time
kairos
chronos
Thesis
Advisor: Harrison Robert
Greenleaf Monika
In vivo gut microbiome sensors

The human large intestine harbors trillions of microbes that interact with one another and the host. In the last few decades, a perturbed gut microbial composition ("microbiome") has been linked with multiple disease processes ranging from autoimmune to neurodegenerative disorders. Different microbiomes produce different chemical environments in the intestine, but understanding how these chemical environments impact host physiology and health is one of the grand challenges of this nascent field. An emerging hypothesis is that disease-associated microbiomes share a common feature: a higher level of environmental oxidation. Unfortunately, testing this oxidation hypothesis directly is currently impossible, because no in vivo redox sensors exist for research animals (e.g. mice and rats). In this work, we propose sensors that measure oxidation-reduction potential (ORP) in the rodent gut. We first motivate the need for in vivo gut ORP data with high spatial and temporal resolution, through new ex vivo and in situ} measurements. We then present the design of wireless sensors that can collect such data and enable gut microbiome studies during dietary, microbial, and genetic manipulation. Sensor features including wireless power-up/wake-up through ultrasonic waves and data communication through galvanic coupling allow for sensor miniaturization, experiment flexibility, and measurement robustness. To demonstrate these features, we implant the sensors in rat ceca and perform the first 12-day in vivo gut ORP measurement. The presented gut microbiome sensing platform paves the way for experimental testing of biological hypotheses, offering new opportunities for understanding redox pathophysiology mechanisms, and facilitating translation to disease diagnosis and treatment applications

Keywords: Electrical Engineering.
Advisor: Arbabian Amin 
Khuri-Yakub Butrus T.
Soh H. Tom