VictorJuiz/thesis huggingface dataset:
A computational framework for learning and transforming task representations
Keywords: Psychology.

Human cognition is fundamentally flexible — we can adapt to novel tasks rapidly. We can sometimes adapt to a novel task without any direct experience on that task, based on its relationship to previous tasks. By contrast, while deep-learning models can achieve superhuman performance on many tasks, they are often unable to adapt to even slight task alterations. This ostensible inflexibility has led to criticism of deep learning models by cognitive scientists. I begin this dissertation by reviewing the literature on cognitive flexibility, and recent advances in building more flexible artificial intelligence systems. I provide a synthesis of these literatures, and outline the challenges that I believe remain. In particular, I focus on the ability to adapt to new tasks zero-shot — that is, without any data — based on their relationship to prior tasks. To address this challenge, I propose a general computational framework for adaptation to novel tasks based on their relationship to prior tasks. The framework is based on meta-mappings, higher-order tasks that transform basic tasks. I propose a parsimonious implementation of this framework in the form of homoiconic meta-mapping architectures. I demonstrate this framework across a wide variety of tasks and computational paradigms, ranging from regression to image classification and reinforcement learning. I compare to both human adaptability, and language-based approaches to zero-shot task performance. I show that meta-mapping is quite succesful, often achieveing 80-90% performance on a novel task, even when the new task directly contradicts prior experience. I further show that using this adaptation as a starting point can dramatically accelerate later learning on a task, and reduce the errors made on the way to mastery by nearly an order of magnitude. Thus, I suggest that meta-mapping can provide a computational basis for adapting to new tasks, and a starting point for efficient learning. This dissertation therefore provides a framework for building better cognitive models and more flexible artificial intelligence systems. In the final chapter, I review the broader contributions of this work to an ongoing discussion about the computational principles necessary for intelligence, and highlight possible future directions ranging from understanding mathematical cognition to neuroscience
Computational imaging with single-photon detectors
Keywords: Electrical Engineering.

Active 3D imaging systems, such as LIDAR, are becoming increasingly prevalent for applications in human-computer interaction, robotics, autonomous vehicle navigation, remote sensing, and more. However, the ability to image with short acquisition times or at long distances is fundamentally limited by the weak signal of backscattered light. Moreover, conventional active imaging systems fail to exploit information contained in multiply-scattered light. In this thesis, we develop novel computational imaging frameworks for photon-efficient 3D imaging and imaging with multiply scattered light using time-resolved detectors that are sensitive to single photons. Our framework for photon-efficient 3D imaging uses convolutional neural networks to jointly process measurements from a single-photon detector and a conventional camera. The approach outperforms existing techniques in a challenging regime where the average number of detected signal photons at each pixel is less than one. Our frameworks for imaging with multiply scattered light efficiently model and invert light transport, enabling new capabilities in non-line-of-sight imaging (i.e., imaging around corners or behind occluders) and imaging through scattering media. For example, we demonstrate significant improvements to the computational efficiency and accuracy of non-line-of-sight imaging, enabling reconstruction of large hidden scenes with unprecedented resolution and fidelity. Our method for 3D imaging through scattering media efficiently models light propagation through a diffusive barrier and outperforms techniques like time- or coherence-gating
Computational nanophotonic design : frameworks and applications
Keywords: Applied Physics.

In photonic inverse design, an automated procedure is used to search through designs that may have unintuitive shapes previously unconsidered by photonic designers. In recent years, the inverse design method has achieved more compact, efficient, and robust devices as compared to traditional photonic devices. However, inverse design has yet to become a standard, mainstream design technique. In this dissertation, we address several of the challenges in transforming inverse design into a practical design tool. First, inverse design devices are typically small in size because of the computational costs associated with rigorously simulating large electromagnetic structures. Here, we demonstrate a fast and accurate simulation for metasurfaces that are hundreds to thousands of wavelengths in size. Second, inverse design devices may exhibit geometries that are difficult to fabricate. Here, we show that appropriately fabrication-constrained inverse-designed devices can be successfully fabricated in commercial foundries. Finally, we develop SPINS, a nanophotonic inverse design framework that emphasizes flexibility and reproducibility. There are four main elements to the inverse design method: structure parametrization, the electromagnetic simulator, the objective function, and the optimization procedure. SPINS enables the designer to easily choose the appropriate elements to use for their particular photonic design problem, thus enabling the inverse design method to be more readily applicable to a larger number of design problems
Computational neuroengineering for artificial retina
Keywords: Electrical Engineering.

A major goal of neuroengineering is to control spiking activity at cellular resolution in a way that replicates neural code. Our context is the artificial retina, a neural interface that aims to treat blindness due to retinal degeneration by interfacing with remaining retinal ganglion cells. Using primate retina recordings from a large-scale multielectrode array as a lab prototype of the device, we study three important problems : (1) How does the retina normally respond to visual stimuli? (2) How do we use healthy retinas to predict responses in a blind retina, in spite of individual variability? (3) How can we replicate this response pattern by electrical stimulation? First, we build a model for visual encoding that incorporates non-linear spatial integration, a prevalent computational motif in neural circuits. We identify subunits that hierarchically partition the receptive field into localized components. For prosthesis application, we extend the method to find shared nonlinear substrates in cell populations and describe their role in explaining responses to natural stimulus. Second, for translating these models to restoring visual function in a blind retina, we study the variation in visual encoding across a hundred recordings from different animals. We learn a low dimensional manifold to separate individual variability from shared computations. Using the manifold, we study the covariation of response properties of different cell-types and observe surprising differences in neural encoding between males and females. The low dimensional manifold also helps in efficiently identifying the neural code in a previously unseen retina, and calibrating device function for a bilnd retina. Finally, we present an approach to replicate neural responses as accurately as possible when the interface has imperfect selectivity. By temporally multiplexing a collection of non-selective patterns, we show improved performance compared to the algorithms used by existing devices. Under simplifying assumptions, a greedy algorithm for identifying the sequence of stimulation is surprisingly close to optimal. This framework allows us to use experimental data to derive insights on the hardware design
Computational Optimization of Solar Thermal Energy Generation and Storage
Keywords: Solar thermal energy storage
energy systems optimization
Thesis

Integrating renewable energy resources into the power generation system is essential for achieving future renewable portfolio standards. Solar thermal is not as popular as solar photovoltaic, but it does enable thermal energy storage (TES), which can provide longer durations of storage than many other technologies. The expense of installation is a significant drawback of solar thermal with TES, but the cost is declining over time. Permitting these operators to sell power at retail utility rates may improve the economics of solar thermal projects.
In this work, we model and optimize a 15 MWe parabolic trough concentrating solar power plant with thermal energy storage. The model simulates power output of a 10 hectare solar field assumed to operate in Daggett, California. We use a proxy model for the solar field, and simulate a three-stage heat exchange process, a thermal energy storage tank with radiative, convective, and conductive heat loss, and a steam turbine. Irradiation data from the National Solar Radiation Database (NSRDB) [4] and electricity price data from the California Independent System Operator (CAISO) [1] are clustered into representative days for the year 2017. We use the Particle Swarm Optimization (PSO) - Mesh Adaptive Direct Search (MADS) hybrid optimization scheme to solve for the optimal operations of the plant. Decision variables include the mass flow rates into and out of storage at hours 7-22 for each clustered day. Constraints include the pinch temperature, approach temperature, steam quality, and temperature ranges of the heat transfer fluid (HTF).
We first consider idealized price curves, for which the optimal operations are intuitive, and observe that optimizer performance agrees with intuition for these cases. We then use the CAISO price data and evaluate the value of two-hour, four-hour, and six-hour storage tanks by optimizing operations over each clustered day to compute the annual profit and net present value (NPV) of a plant over 30 years. We find that the four-hour storage tank is optimal, while the high installation cost of the six-hour storage tank renders it suboptimal despite a higher annual operating profit. Lastly, we conduct a sensitivity analysis to compare the base case, a “fatter” duck price curve, and Power Purchase Agreements (PPAs). We find that a six-hour storage tank yields the highest NPV with the more volatile prices of the “fatter” duck curve, as the higher peaks in electricity prices offset the higher cost of a six-hour storage tank. A plant operating under a PPA without storage is found to be the least profitable, suggesting that it is beneficial to utilize TES and sell at market prices rather than enter into a PPA.
Computing stationary distributions : perron vectors, random walks, and ride-sharing competition
Keywords: Management Science and Engineering.

Stationary distributions appear in a wide variety of problems in areas like computer science, mathematics, statistics, and economics. The knowledge of stationary distribution has a crucial impact on whether we are able to solve such problems, or how efficiently we can solve them. In this thesis, I present three of my projects which have a common theme of dealing with stationary distributions. In the first work, called Perron-Frobenius theory in nearly linear time, we give a nearly linear time algorithm for computing stationary distribution of matrices characterized by the Perron-Frobenius theorem. Through our algorithm, we non-trivially extend the class of matrices known to be solvable in nearly linear time by graph Laplacian solvers. In the second work, called high precision small space estimation of random walk probabilities, we try to make a step toward derandomization of the complexity class RL or randomized log space, which is a long-standing open problem in complexity theory. In this context, the knowledge of stationary distribution for directed graphs seems to be a barrier in achieving such a result. While we are not able to resolve L vs. RL problem, we give a small space deterministic algorithm to estimate random walk probabilities to high precision in undirected graphs, as well as directed Eulerian graphs. In the last work, with the rise of ride-sharing platforms, we study a set of important questions naturally arise in this context. Although we use a different set of technical tools compared to the previous two works, we still deal with characterizing equilibria in stationary systems. One of the main questions we answer is whether the competition between two platforms can lead to market failure by pushing the drivers out of the market
Conditional independence testing for variable selection and causal inference
Keywords: Statistics.

Conditional independence testing is a central statistical topic, and here we report on recent advances leveraging such tests for variable selection and causal inference. First, we consider the generation of model-X knockoffs, developing an efficient sampler for arbitrary graphical models as well as proving a lower bound on the computational complexity of knockoff sampling. Our new knockoff sampler enables the deployment of the principled model-X knockoff framework for variable selection for a wider range of problems. Second, we turn to variable selection with compositional data, developing a penalized fitting procedure and a conditional inference procedure to asses goodness-of-fit. We also briefly discuss the connections between model-X knockoff testing and conditional inference. Lastly, we consider the selection of causal variables in genetics. Using data from parent-offspring trios, we formulate inheritance as a high-dimensional randomized experiment, explain how this corrects for confounders in genome-wide association studies, and develop a set of conditional independence tests compatible with multiple testing procedures to check for causal variants in multiple regions of the genome
Confronting Settler Colonial Histories: Education, Race and Politics in the Hawaiian Kingdom
Keywords: settler colonialism
education
Hawaii
Hawaiian Kingdom
Thesis

What was the role of Hawaiian aliʻi in establishing educational institutions and encouraging literacy prior to the overthrow of the Hawaiian Kingdom in 1893? Existing literature on the history of education in Hawaii portrays a linear narrative of foreign domination in which missionaries arrived in Hawaii and imposed western modes of learning and governance on Native Hawaiians. More recently, Hawaiian scholars have countered these dominant narratives by documenting the Hawaiian monarchy’s agency in actively adapting western political strategies, in part to protect their sovereignty from foreign imperialism. This thesis builds upon this recent Hawaiian scholarship and shows how Hawaiian leaders supported early missionaries as teachers of literacy and Christianity as part of an ongoing project of cultural and political adaptation. Using the framework of settler colonialism, I address how existing literature ignores Hawaiian agency and explicitly supports the American project of occupation. I then challenge this dominant narrative, relying on primary materials that show the major role of Hawaiian ali'i and teachers in the adoption and spread of literacy in Hawaii. I argue that the aliʻi supported Western education in the Hawaiian Kingdom as part of a foreign policy strategy to resist Western imperialism. An analysis of archival, primary source material indicates that the aliʻi supported palapala as a political tool, resulting in Western education as a crucial component of leadership and government positions.
Connecting America: An Analysis of the FCC's Connect America Fund Phase II Auction
Keywords: Broadband Expansion
Connect America Fund
Universal Coverage
Maximizing Benefits
Cost Efficiency
Thesis

The FCC’s Connect America Fund (CAF) Phase II auction is the largest broadband expansion program to date, awarding $1.5 billion in subsidies over the next 10 years. The FCC set two policy objectives for this program: (1) achieve universal internet service access; and (2) maximize the value Americans receive, balanced with cost efficiencies. This thesis analyzes the extent to which the CAF auction achieved the FCC’s second policy objective by estimating the effects of reserve price per household on predicting which census block groups received bids in the CAF auction. Internet service providers placed more bids in areas with a higher reserve price per household, not in areas with higher expected demand. While cost efficient, this bidding behavior does not maximize the total value Americans will receive from the auction. To better align future internet expansion programs’ results with the FCC policy objectives, I make the following recommendations: (1) encourage more satellite-based providers to participate in future auctions; (2) re-evaluate reserve prices to align providers’ and the FCC’s estimates of costs; and (3) limit the total amount of support a provider may bid for in the auction.
The constraints of progress : college feminization and the value of the college degree
Keywords: Sociology.

This dissertation investigates how the status and value of the college degree as a credential are constructed and evaluated in response to the changing social demographic compositions of colleges and universities. A large body of scholarship shows that occupations with a higher proportion of women have lower average rewards for both women and men—a finding many gender scholars argue is the result of the devaluation of work associated with women. In this dissertation, I ask whether a similar process occurs when women earn a higher proportion of college degrees. One notable implication of women's rising educational attainment is that many colleges and universities, like occupations, are increasingly understood in public and academic discourse to be undergoing a feminization of their own. How does the feminization of colleges and universities shape perceptions of college graduates from institutions where women earn most degrees? Is the value assigned to their credentials lower than that assigned to credentials from non-feminized institutions? I draw on original survey-experimental data collected from a representative sample of U.S. adults to test how the gender composition of men's and women's degree-granting institutions affects their chances of being selected for a job interview. This research offers evidence that the feminization of colleges and universities has very different labor market consequences for male and female graduates. I find that college feminization shapes perceptions of college value in the labor market in two distinct, but interrelated, ways. Unlike occupational feminization, college feminization does not lead to an across-the-board devaluation of the college credential. Rather, devaluation unfolds as a gendered process. Although college feminization has little impact on whether women are recommended for an interview, men who have attended feminized colleges are penalized in interview recommendations. Next, I show that college feminization and applicant gender shape the ways in which college and non-college-educated participants construct merit when evaluating job criteria as important factors in the screening process. This work suggests that the specific job criteria that are tied to employment outcomes for applicants is highly dependent both on the college status of evaluators and the context in which their characteristics as applicants are evaluated. Together, this work highlights the emerging role of feminization in the higher education landscape and suggests several paths forward for scholarship on feminization and its role in exacerbating existing inequalities among college graduates
Contact, conflict, and social cohesion
Keywords: Political Science.

Can intergroup contact build social cohesion? A rich literature demonstrates that intergroup contact typically reduces prejudice, but we have little evidence on whether contact can change behaviors in real-world settings. This is particularly true when ethnicity is highly salient. This dissertation provides causal evidence on these questions by leveraging studies of Iraqis displaced by ISIS, American schoolchildren, and British soccer fans. I find that diverse forms of contact — from face-to-face contact to virtual exposure — generally improve intergroup relations, even after ethnic violence. I also find, however, meaningful variation in the extent to which these effects extend to social contexts outside of the intervention. I propose that the salience of intergroup conflict moderates the returns to contact. The positive impacts of contact may struggle to change intergroup relations where such change is most needed, but can nonetheless build the micro-foundations of social cohesion in important ways. In shedding light on how contact operates, this dissertation enriches our ability to generate expectations about the effectiveness of prejudice reduction interventions, and ultimately brings us closer to understanding the policy tools available to reap the rewards of intergroup cooperation
Continuous Improvement in Practice: Findings from Schools and Districts in California
Keywords: Continuous Improvement
Improvement Science
School leadership
District leadership
CORE districts
PACE
Stanford Graduate School of Education
Policy Organization and Leadership Studies
Thesis

Prepared for Policy Analysis for California Education (PACE), this research brief reports effective strategies that enable continuous improvement capacities at the school and district levels. Through comprehensive analysis of case studies and interviews with school leaders, this brief highlights salient practices that are visible within four lenses of continuous improvement. The findings of this report can be shared with instructional leaders who are hoping to utilize improvement science within their organizations and can be replicated or adjusted given different school or district contexts.