VictorJuiz/thesis huggingface dataset:
Crafting illiberal Europe. Legitimation, mobilization, and mass influence in the Soviet and Nazi occupied Baltic States, 1939-53
Keywords: History.

From 1939 to 1953, the Soviet Union and Nazi Germany took turns occupying the Baltic States of Estonia, Latvia, and Lithuania. This dissertation investigates how the Soviets and Nazis legitimized their rule and mobilized hundreds of thousands of ordinary Balts for their state-building and war effort. The Baltic context offers a unique setting for a comparison of the Soviet and Nazi polities at a time when both states sought to revise the interwar European order and remake the continent after their own images. Soviet and Nazi expansion to the Baltic States has usually been studied in its destructive capacities. This dissertation argues that it is equally important to explore the regimes' "constructive" facets: how the two regimes succeeded in genuinely legitimizing their rule, sealing alliances, even positing the Baltic region as a model for all of Europe. Instead of approaching the regimes as sui generis entities, the dissertation explores their entanglement with each other and the linkages they built with local societies, highlighting the agency and participation of the local population. In particular, the dissertation scrutinizes the Soviet and Nazi fixation on mass influence, the shaping and transforming of local attitudes and behavior. It contrasts the Soviet maximalist practice of "mass political upbringing" with the "looser" Nazi notions of "war propaganda." The Soviets sought not only to bombard the audiences with political propaganda but to transform them into disciplined consumers of political education. The Nazi "war propaganda, " by contrast, relied heavily on local participation, leaving local ethnic majorities vast opportunities for self-expression and nation-building, a wartime home front culture that continues to inform Baltic memory to this day
Cross-boundary design work : ethnographies of co-design across knowledge and organizational boundaries in professional engineering practice
Keywords: Mechanical Engineering.

In a world of boundaries, silos, and division, engineers have been called to address so-called "wicked" design challenges such as energy insecurity and inequity that are ill-structured, complex, and tied to multiple groups of stakeholders and potential contributors. As engineers, we are typically untrained in facilitating meaningful engagement with people beyond our knowledge group and organizational boundaries—engaging members of other groups as design contributors, not just as design consultants or recipients of designs. This thesis works to build theory on how designers within organizations and potential design contributors within and outside those organizations span their knowledge group and organizational boundaries as they co-design together. The findings are based on inductive grounded theory analyses of ethnographic fieldwork over two-five years, some of which was collected by a team of researchers, in partnership with design firms in the energy and automotive industries. The main contributions of the thesis are twofold. Firstly, I build theory on how product development professionals engage with objects and each other to support collaborative inquiry, discovery, and design across knowledge group boundaries. Secondly, I build theory on how co-creative capacity is developed or diminished between a firm and an open online community. This work answers recent calls from within the design research community to move toward "theory-driven" research. I build directly upon theories from organization studies, namely "absorptive capacity" and "objects of collaboration." Additionally, the work contributes to a sparse but growing literature that uses a multi-year ethnographic approach with practicing engineers in design organizations in contrast to studies with students or based on surveys, interviews, or controlled experiments that provide only snapshots in time or are divorced from professional design contexts. Taken together, the findings have practical implications for how those engaged in product development can build co-creative capacity, use objects of collaboration, and engage across knowledge group and organizational boundaries to co-design a better world
Crosslinking strategies for 3D bioprinting of engineered hydrogels
Keywords: Chemical Engineering.

3D bioprinting has demonstrated potential for spatially patterning cells and materials together to create structures that recapitulate native tissue. By mimicking these structural features, bioprinted constructs can serve as in vitro models for basic research or can be implanted into the body for regenerative medicine applications. However, this technique is limited by a lack of suitable soft materials for use as bioinks since it remains challenging to design materials that are both printable and can support cell culture. In addition, as matrix material properties are known to influence cell phenotype and behavior, it is becoming increasingly important to tailor the bioink's properties to each individual cell type in order to create more complex, functional printed structures. In this thesis, I explore how engineered, tunable hydrogels can be used to address the current limitations of 3D bioprinting and then develop crosslinking strategies to control the mechanical, biochemical, and dynamic behavior of bioink materials before and after printing
Cruel and Unusual: Contesting Human Rights Violations in the American Carceral System
Keywords: Mass Incarceration
Prisons > Law and legislation
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Thesis

Lindsay Newfeld’s capstone project investigated the lack of avenues available to incarcerated individuals in the United States who contest human rights violations occurring during their confinement. Her paper specifically focused on two available avenues for redress - prisoner grievance mechanisms and civil litigation and sought to answer (1) why these processes are so ineffective at producing remedies, and (2) the justification for denying this population effective access to the courts. Through interviews with formerly incarcerated individuals from Illinois, Lindsay gave insight to the depth, texture, and the perspectives of those who have experienced human rights violations while incarcerated. Motivated by her advocacy through various criminal justice and prison reform organizations, Lindsay hopes to understand prisoners’ rights through a human rights framework and create tangible change within the prison system.
Cyber risks in networked autonomous systems
Keywords: Management Science and Engineering.

Operation of autonomous unmanned vehicles introduces new risks about which decisionmakers have neither exhaustive statistics nor similar systems from which to derive priors. This model-based risk analysis combines algorithms used for autonomous control, Monte Carlo simulations, and learning parameters from data to improve the risk model's performance. The results inform high-level decisionmakers on when and how best to employ autonomous unmanned vehicles in security and military applications where risk tolerance is higher than for civilian applications, while explicitly maintaining high-risk decisions as the responsibility of human decisionmakers, even when software is used in the process of executing those decisions. This risk analysis has applications in use of unmanned vehicles for localization of radio-frequency threats and maritime tracking of non-cooperative targets using linear array sonar
Cytokine receptor engineering for mechanistic and therapeutic studies
Keywords: Bioengineering.

The activity of immune cells is regulated by proteins on the cell surface called cytokine receptors. These proteins allow cells to grow in a controlled manner and to balance immunosuppressive and stimulatory activities. My dissertation has focused on engineering cytokine receptors to understand their function and to target cells of interest therapeutically. I first focused on the interleukin-2 receptor (IL-2R), both to engineer orthogonal activity for selective cell growth, and to determine how unique signals are transmitted when binding to interleukin-15 (IL-15) and IL-15 receptor (IL-15R) complexes. I then focused on the colony-stimulating factor 3 receptor (CSF3R) which controls the growth of neutrophils, the most abundant type of white blood cell. I investigated if a mutated CSF3R epitope could be targeted therapeutically, why this amino acid substitution is the predominant mutation in chronic neutrophilic leukemia, and how it leads to uncontrolled neutrophil growth. Combined, these engineering approaches highlight the function and therapeutic potential of cytokine receptors
Data, digital risks, and financial markets
Keywords: Business.

Big data and pervasive digitization have transformed both the risks corporations face and the methods used to insure against them. What does this mean for the existence and efficiency of markets for risk-sharing and speculation? In my dissertation, Data, Digital Risks, and Financial Markets, I model algorithmic approaches, data dimensionality, and novel hazards to modernize our understanding of insurability, information aggregation, and optimal regulation in financial markets. The dissertation consists of three chapters. The first chapter (joint work with Yilin (David) Yang) studies the equilibrium coexistence, in financial markets, of traders using high-dimensional datasets and algorithmic strategies. The second chapter describes the extent to which insurance in markets for new or nonstationary risks exists and when government data policy can help. The third chapter argues how insurance availability might actually worsen ransomware, a pressing new cyberrisk, and presents sublimit caps as a practical regulatory measure
Data driven generative accelerator model
Keywords: Physics.

With the development of accelerator physics and synchrotron radiation technology, relativistic electron beams accelerated by a linear accelerator (Linac) are capable of amplifying shot noise and radiating an electromagnetic field in the X-ray regime. This system is referred to as the X-ray Free Electron Laser (XFEL) under self-amplifying spontaneous emission (SASE) mode. Serving as the first XFEL facility in the world, the Linac Coherent Light Source (LCLS) has been operating at the SLAC National Accelerator Laboratory (SLAC) since 2009 and benefiting various fields of photon science. Accumulating more than a decade of data, we have collected many experimental records and established systematic coding packages of simulation. Driven by this data, researchers can build surrogate models in order to efficiently and reliably reproduce radiation. In addition, well-trained accelerator models can also support other learning algorithms or guide facility pre-tuning. In this dissertation, we first discuss the SASE XFEL system by solving the radiation field under a one-dimensional approximation. The statistical optic theory helps us to evaluate the distribution and correlation properties of the radiation power profiles. In addition, we introduce learning-based generative algorithms and how they inspired us to use generative adversarial networks (GAN) to build XFEL surrogate models. Next, we show five datasets from various simulated or experimental environments. Lastly, data-driven accelerator models can stably converge and efficiently reproduce training datasets. These models are further investigated by statistical diagnosis and network analysis. In summary, this research implements a XFEL generative model driven by simulated and experimental power profiles data. The algorithmic ideas and surrogate models are promising techniques for data-supporting and facility tuning
Data-driven energy efficiency and flexibility in commercial buildings
Keywords:  Energy Resources Engineering
demand response, energy efficiency, buildings
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Thesis

Commercial buildings offer significant potential to reduce cooling load by raising zone temperature setpoints. This is useful for demand response, avoiding blackouts during heat waves, and during normal operations to reduce costs, water usage, and emissions. We performed experiments in seven commercial buildings on the Stanford University campus. We found that buildings responded differently to temperature setpoint adjustments with load reductions ranging from 4 to 29 percent. This work explores the drivers for response heterogeneity by analyzing equipment-level behavior. Distributed sensor data throughout the buildings is used to diagnose the behavior of Air Handling Units, Variable Air Volume systems and Fan Coil Units. The main driver for load reduction was changes in chilled water valve positions, which differs from previous experiments reported on in literature. One possible explanation is that the HVAC equipment in these buildings implement the most recent ASHRAE standards of supply air temperature (SAT) reset through logic called Trim-and-Respond. This finding suggests that new building control sequences could alter conclusions from previous DR field experiments. It is also found that the interaction of ventilation and cooling requirements can impact operational energy efficiency, sometimes causing over-cooling of certain zones. Over-cooling is quantified in terms of temperature difference and the cooling load that is wasted by keeping zone temperatures below the cooling setpoint. We also find that dominant zones with high cooling requirements play an important role in determining building flexibility. Dominant zones included in experiments can drive large load reductions observed from setpoint changes. If the zone is excluded, the building will not be responsive to setpoint changes. High flexibility is seen from buildings without ventilation constraints and buildings with dominant zones included in experiments. This is true even if the building has high levels of wasted cooling. In this case, the building is inefficient but has high flexibility. Buildings with ventilation constraints or dominant zones excluded from experiments are less dependable flexibility resources.
Data-driven methods for improving social interaction in virtual environments
Keywords: Mechanical Engineering.

The COVID-19 pandemic highlighted the value of remote social communication between individuals. The fundamentals of such communication are actively studied for virtual reality interaction, remote video calls, and social networking, but research on these methods infrequently focuses on encoding explicit social use-case. In addition, systems that do incorporate modalities for explicit social information, such as touch, are often hand-tuned or manually generated. In this work, we seek to leverage data-driven methods to improve social interaction between individuals in virtual environments. In order to socialize effectively in shared virtual environments, we seek to learn how a person physically interacts in such environments. For the first contribution of this thesis, we develop the interaction-expectation model to improve hand tracking and interaction. The purpose of this model is to predict hand-object interaction before such interaction occurs. This allows for smooth interaction in virtual environments, which can be used to improve haptic feedback in shared-object settings. We find that we are able to predict human-object interaction before it occurs over short timescales (approximately 100ms). In order to improve social interaction, we must also understand the emotional intent of actions between individuals. In the second contribution of this thesis, we collected a dataset of pairs of individuals interacting to convey affective information through touch. We record the data using a soft pressure sensor on one participants arm. This dataset was collected in a more natural environment than existing ones, and utilized scenario prompts rather than single word prompts. For the third contribution we then develop a system to automatically convey social touch information using our dataset. We develop an algorithm that leverages computer vision algorithms to map from the recorded data to an actuator sleeve with an array of actuators which indent the skin. We find that humans accurately interpret the affective intent of our system with accuracy comparable to human touch interaction. Finally, we consider the visual mode of social experience by improving affective facial expressions for 2D virtual avatars. In recent years socialization via virtual avatars has dramatically increased, with the growing use of 2D drawn, rigged, virtual avatars with face tracking. As a last contribution of this thesis, we create a novel dataset of 2D avatar expressions, of high quality and richer data than previous datasets. We then propose use cases for this dataset to automate the creation of 2D avatars. Through the collection of contributions in this thesis, we seek to push the field of virtual social interaction forward with multi-modal interaction. This will allow people to interact in virtual environments, connect with remote loved ones, and represent a version of themselves online more easily and effectively
Data-driven modeling of neurodegeneration in Alzheimer's disease
Keywords: Mechanical Engineering.

Alzheimer's disease constitutes one of the biggest public health challenges of our time. About one out of nine Americans over the age of 65 is affected today, with numbers rising steeply as our demographics shift towards an older society. The hallmarks of Alzheimer's disease entail an abnormal aggregation of the proteins amyloid-beta and tau in the brain, widespread brain atrophy, and gradually advancing cognitive impairment. The development of disease-modifying treatments requires early diagnosis and a deep understanding of disease causes and progression. However, to date much of our understanding of the early disease mechanisms and their interactions is vague and based on qualitative observations. In this thesis, we introduce computational models describing the prion-like aggregation of protein, the accumulation of pathological tau protein in the brain, and the coupling between tau pathology and regional tissue atrophy in Alzheimer's disease. Validating these models with longitudinal imaging data of real subjects allows us to identify numerical ranges for disease parameters on a personalized basis, thereby shifting our understanding of pathological events and causal relationships to a quantitative level. We leverage hierarchical modeling and Bayesian inference to take advantage of group level similarities and quantify uncertainty in our simulations. With the calibrated models we are able to generate personalized predictions forecasting how tau pathology and atrophy may evolve in a certain patient over time. Our approach also enables us to discover supporting evidence that the presence of amyloid-beta is a driver for tau pathology and tau-induced atrophy
Days Gone Forever: Women’s Role in the 1936-37 Flint Sit-Down Strike and its Influence on the United Auto Workers Union
Keywords: International Union, United Automobile Workers of America (CIO)
General Motors Corporation Sit-Down Strike (1936-1937)
General Motors Company
Labor movement
Dollinger, Genora Johnson
Jeffrey, Mildred, 1911-2004
Kraus, Dorothy
Reuther, Victor G. (Victor George), 1912-2004
Martin, Warren Homer, 1902-1968
Strikes and lockouts
Women > Employment
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Thesis

This senior thesis discusses women’s role in the 1936-37 Flint sit-down strike, long seen as one of the pivotal events in bringing unionization to the nation’s automotive industry. It argues that women’s experiences during the strike and contributions to its success resulted in fundamental structural change in the United Auto Workers (UAW) union in subsequent years.
Decision-making with matrix-shaped data
Keywords: Business.

Research on matrix-shaped data has focused on conducting statistical inferences from a matrix based on a sparse number of its observations in a high-dimensional setting, with important applications for recommendation systems, revenue management, marketing, and healthcare operations. In response to the increasing popularity of online platforms, experimentation and causal inference, this thesis addresses the following new and pressing problems: how to leverage non-uniform data patterns in a matrix to recommend favorable products and estimate treatment effects more accurately, how to adapt low-rank matrix structure in online experimentation to save costs, and how to better optimize directly from the matrix-shaped data without the estimation interlude. The first part of this thesis starts with an algorithm called NU-Recommend for learning user preferences to optimize recommendation systems using non-uniform matrix-shaped data. It then ventures into the causal inference domain and applies the NU-recommend algorithm to recover missing values in the panel data, leading to more accurate "synthetic control" methods to measure the efficacy of treatment as the missing pattern in the panel data is highly non-uniform. Online learning is state-of-the-art practice in many companies, as they have widely adopted online experimentation such as multi-armed bandit (MAB) to seek the best product for their customers among periodically refreshed product catalogs. This thesis then introduces the Low-Rank Bandit algorithm that can reduce the opportunity cost of online experimentation by leveraging low-rank structure in matrix-shaped data. This allows for a transition from conducting a one-time prediction with offline data as performed by NU-Recommend to conducting prediction online as the data is collected in real time. Finally, this thesis goes beyond the traditional "estimate then optimize" regime which all previous three parts have followed, and instead designs a direct method that goes from data to decision in one step. This method paves the road for future work of going directly from matrix-shaped data to decision making by leveraging the properties of the matrix structure
Deep generative modeling for structure-guided protein design
Keywords: Biochemistry.

In recent years, deep generative modeling has garnered attention as a powerful way to approximate the probability distributions of high-dimensional data, and synthesize an infinite amount of novel data from finite datasets. In the field of protein design, where structural data is low in abundance and expensive to obtain, generative modeling has the potential to "fill" many gaps in known conformational space, increasing the scope of designable proteins, and allowing us to automate design in a data-guided way. This thesis describes my work in developing and training generative models for computational protein design, and describes the reformulation of a general protein design task as a conditional generation problem, solved by latent variable optimization within a deep generative model. I begin with a description of early explorations of protein representation and computer vision methods, leading to the development of a new 3D generative model for proteins, and culminating with the invention of an algorithm that uses generative design to create binding proteins against arbitrary user-specified epitopes. We name this algorithm "Sculptor"