VictorJuiz/thesis huggingface dataset:
Age-related differences in time-use preferences : effects of motivational changes on decisions about time expenditure
Keywords: Psychology.

As populations around the world age and human life expectancy is exceeding traditional social timetables, understanding age-related differences in time-use decisions is key to making the most of long lives. Life-span developmental theories postulate that selectivity, a life-long process of increased focus goals that fit existing constraints and opportunities, is at the heart of limited engagement with activities in older ages. In this dissertation, I test theoretical postulates based on socioemotional selectivity theory (Carstensen, 1993; 2006) explaining age-related differences in preferences for activities. I describe three postulates: 1) Age is associated with a preference for emotionally meaningful activities; 2) Age differences in perceived time horizons are guiding age-related preferences; 3) Time use decisions are more important for older adults than they are for younger adults because time is more precious when it is limited. In Study 1 (N=177, ages 18-93), I find that age is associated with experiencing more meaning in daily activities. I further find that this association may account for better emotional well-being with age. In Study 2 (N=289, ages 18-82) I developed a novel paradigm to examine age differences in preferences for activities. I find that older adults prefer emotionally meaningful activities over activities that satisfy future-oriented goals and give more weight than younger adults to emotional meaning in decisions about how to use their time. In Study 3 (N=555, ages 20-75) I manipulated time horizons in the specific context of work and examined their effect on preferences for work-related activities among employed adults. I find that when time horizons are left unspecified age is associated with a preference for helping colleagues over working on projects that serve future-oriented goals. As predicted, expanded time horizons reduced this preference, but so did limited time horizons. In Study 4, I examine whether older and younger adults approach decisions about new activities differently. I find that, consistent with my predictions, older age is associated with looking for more information about benefits of activities, and that the benefits older adults are interested in are emotionally meaningful. Findings from the studies in this dissertation form the basis on which to build future theoretical models explaining age-related selectivity in preferences for activities. Findings suggest that older age is associated with a focus on emotionally meaningful rewards. Findings suggest that older adults give more weight to emotional meaning in their time-use decisions than younger adults and give relatively little weight to other types of rewards. Findings further suggest that older adults may require more evidence that activities are worthwhile before committing to them than younger adults. Finally, findings highlight the role of future time horizons in shaping preferences for activities in different context. Theoretical and practical implications of these findings are discussed
Aire, Tierra y Fuego: Environmental Trauma and Grassroots Mobilization in Puerto Rico
Keywords: Puerto Rico
Identity
Grassroots Mobilization
Environment
Intersectionality
Ecofeminism
Politics and government
Social surveys
Text
Thesis

“It’s not easy. I cry at night. I cry in the bathroom [...] I have to be strong.” Those
are words uttered by Diana Fernandez. According to the National Oceanic and Atmospheric Association, Hurricane María’s economic loss is estimated to be anywhere from US $43 to $159 billion. The cyclone leveled power grids, homes and other essential infrastructure in Puerto Rico. Scholarly work surrounding the impacts of hurricanes on the island have been widely documented. However, dedication to the storm’s effects on shaping social issues, identity, and political parties are seldom. This study applies text data of hurricane survivors and their testimonials, engages in empirical approaches to identity and leverages qualitative case studies of ecofeminism and the Movimiento Victoria Ciudadana to center gender disparities and grassroots mobilization in Puerto Rico. At its simplest, this study argues that environmental phenomena inform salient discourses of political status and social life on the island—because for many, like Diana Fernandez, life was, and still is, not easy.
Algorithmic techniques for neural network training on memory-constrained hardware
Keywords: Electrical Engineering.

Deep learning has enabled accurate inference for many real world problems, making it an attractive tool for embedded devices at the edge. Unfortunately, these devices are often limited by tight energy and memory budgets, prompting past research to optimize for neural network inference efficiency and size. In our work, we take some of the first steps towards enabling gradient descent-based neural network training on these inference-optimized devices. For example, power-of-two quantization is commonly used to reduce weight size, but training on quantized weights is known to be difficult. We discuss some potential causes of this difficulty and propose improved quantization techniques to enable training. More recently, emerging memory promises to obviate off-chip data movement, but is expensive to update. We present an algorithmic compression technique to enable training without incurring overwhelming write costs of emerging memory technology. When applying these techniques to perform transfer learning on CHIMERA, a chip with large scale resistive RAM (RRAM) macros, we observe a 101x reduction in the number of weight update steps and a 340x reduction in energy-delay product compared to a stochastic gradient descent baseline that achieves the same accuracy
Algorithms for multi-agent robotic assembly planning
Keywords: Mechanical Engineering.

This thesis presents algorithms for multi-agent robotic assembly planning in automated manufacturing contexts. Our work touches on many pieces of the "factory autonomy stack." The first contribution of the thesis is the introduction of a discrete factory problem formulation that includes time-extended multi robot task allocation, inter-task precedence constraints, and collision-avoidance constraints. We propose an efficient method to solve such problems. The two keys to our algorithm's efficiency are its decoupling of task assignment and route planning, and its ability to take advantage of situations where some robots can be delayed in their own schedule without causing any negative impact on the factory's overall performance. The next major contribution of the thesis is a family of replanning algorithms geared toward an online version of our discrete factory problem. In the online setting, the factory command center periodically receives new manufacturing workloads that must be quickly incorporated into the overall schedule. We show through an extensive set of experiments that our replanning methods are applicable across a wide range of problems. Moreover, our proposed methods can be applied in such a way that the factory will never have to freeze while waiting to receive an updated plan. Our final contribution is a proof-of-concept system for large scale multi-robot assembly planning with assemblies and raw materials of arbitrary shapes and sizes. Our system starts with raw materials and a bare-bones set of instructions about how those materials fit together. The planner then synthesizes a construction plan, which defines how each payload will be carried (by one or more robots), where each assembly and subassembly will be built, and which specific robots will be assigned to each solo and collaborative transport task. Finally, a reactive collision-avoidance control policy enables the robots to execute the construction plan in a distributed manner. We demonstrate in simulation that our system can synthesize---in minutes---construction plans for assemblies with hundred of components. Though we do not address all of the relevant ``real-world'' considerations that surround multi robot manufacturing, our work is a small step toward large scale automated construction with mobile robots
The allostery of transglutaminase 2 and its role in coeliac disease
Keywords: Chemical Engineering.

Coeliac disease (CeD) is an inflammatory autoimmune disease triggered by the ingestion of gluten that classically presents with abdominal pain, malabsorption, and diarrhea. Virtually all coeliac patients carry human leukocyte antigen (HLA) DQ2 or DQ8 alleles; gluten peptides bind to HLA-DQ2 or HLA-DQ8, and in coeliac patients, this event results in a T-cell response. Gluten peptides contain many glutamine residues which are deamidated in the small intestine by the ubiquitous protein transglutaminase 2 (TG2), and while native gluten peptides are antigenic to coeliac patients, deamidated gluten peptides are considerably more antigenic. However, TG2 is normally inactive in this environment. Being an abundant protein, TG2 possesses an intricate set of allosteric regulatory mechanisms that governs its activity in its many environments. Careful study of TG2 allosteric regulation is integral for a more complete understanding of CeD pathogenesis, and can thus inform the development of more effective therapies or a cure. Additionally, CeD serves as a model autoimmune disease for our study of other autoimmune diseases, as its disease state can be induced with the introduction of an exogenous protein
American Farmers: Decline and Rekindled Hopes of Small-Scale Agriculture
Keywords: Agriculture > Research
Rural Economies
Text
Report
Thesis

Javier Matta developed a community-engaged capstone project working with the Agriculture and Land-based Training Association (ALBA). This collaborative project involved analyzing the economic data behind the decline of the American agricultural sector, rural economies, and the historic goal and present status of including immigrant farmers in leadership positions in agriculture. More importantly, through this research, Javier hopes to advocate for increased investment in small-scale farming initiatives that are immigrant-led, as modeled by ALBA programs.
American Orthodox Jewish outreach through higher education : three strategies for fashioning a post-Holocaust Judaism
Keywords: Education.

This dissertation charts three strategies undertaken by American Orthodox Jews to fashion a Judaism for the newly college educated Jewish masses in the decades following the Holocaust. By examining the ways in which modern Orthodox, Chabad Hassidim, and Haredi Jews turned to the language, structures, and institutions of American higher education to secure a place for Judaism in American life, this thesis uncovers the ways in which each group reimagined their underlying ideological frameworks for a new context and new audience. The analysis begins with the mid-century expansion of Yeshiva University, specifically the founding of the James Striar School, the first Orthodox residential college (yeshiva) designed for newly or prospective Orthodox Jews (ba'alei teshuva). It takes up the intellectual work of the school's founder, Rabbi Dr. Samuel Belkin, and its director, Rabbi Morris Besdin, in reorienting an institution of higher education for religious purposes. The second example delves into the growth of para-university religious houses, specifically the Chabad House. Designed by Rabbi Shlomo Cunin and theologically informed from the teachings of sacred Chabad texts as marshaled by Rabbi Menachem Mendel Schneerson, the Chabad House offered a counter-cultural religious message intentionally deployed at the margins of campus and society. The third instance attends to the institution of science -- specifically mathematical statistics -- in the research program and pedagogical efforts of the seminary Aish HaTorah. By examining the writings and lectures of the school's founder, Rabbi Noah Weinberg, in the larger context of Aish's outreach program, this study reveals how research institutions can be imagined and appropriated as a means of reviving the Jewish faith. At stake in the efforts of each sub-denomination is a bold claim on the mantle of authenticity of Jewish tradition. Such commitment to conservation compelled the development and authorization of creative hermeneutical link between these theological and institutional innovations to historical and scriptural origins. This intense engagement with higher education in three different forms illuminates the epistemological contours of a conservative American religion in the decades following the twin existential crises of the Holocaust and the ascension of a twentieth-century American secularism
Amino acid-derived dynamic oligomers for the intracellular delivery of mRNA
Keywords: Chemistry.

RNA technology is transforming life science research and medicine, however, many applications are limited by the accessibility, cost, efficacy, and tolerability of delivery systems. We recently reported a new class of gene delivery vectors with a property that makes them particularly well-suited to binding to, transporting, and releasing mRNA: charge alteration. The amphiphilic co-oligomers, synthesized via organocatalytic ring-opening polymerization (OROP), consist of a cationic oligo(N-hydroxyethyl glycine) block and readily form complexes with polyanionic mRNA. Under conditions at which the backbone is partially deprotonated, the block undergoes an irreversible depolymerization to liberate neutral small molecules. We call these materials charge-altering releasable transporters (CARTs), and they have shown remarkable efficacy in delivering mRNA in cultured cell lines and in living mice, showing a high level of protein expression localized to the spleen when administered intravenously (IV). Here, I describe two new classes of CARTs that demonstrate the versatility and tunability of this approach. I first demonstrate that the charge-altering strategy is generalizable with the development of CARTs based on oligo(serine esters). These Ser-CARTs, which rearrange to form serine-based peptides, are effective in vitro and deliver mRNA localized to the spleen. The second class is derived from the amino acid ornithine. CARTs based on oligo(N-hydroxyethyl ornithine) (O-CARTs), which contain ionizable amines on both the backbone and the side chain, perform as well or exceed the performance of previously developed CARTs in vitro. Remarkably, when administered IV, O-CART/mRNA particles show high selectivity for the lungs, and simple changes to the formulation result in a shift in the biodistribution profile to favor either splenic or systemic protein expression. These new delivery vectors demonstrate the promise of developing platform technologies in the advancement of mRNA therapies
An investigation into the environmental parameters influencing recognition learning and memory in rodents
Keywords:  Department of Biology
Biology
Neurobiology
Sleep deprivation
Circadian rhythms
Memory consolidation
Thesis

Decades of research on species across the animal kingdom highlight the importance of sleep for optimum learning and performance of cognitive tasks. The sleep-memory consolidation hypothesis characterizes sleep as a brain state optimal for stabilizing processes that strengthen and integrate a memory into a long-term store. Although this is widely accepted at a physiological level, a nuanced understanding of how sleep facilitates memory consolidation is yet to be achieved. Of particular interest is demystifying how the homeostatic sleep drive and circadian system both singly and jointly influence memory performance. Other relevant considerations include the roles of the performance measure, task complexity and methodology. The present study investigates whether, and to what extent, variable combinations of experimenter handling, sleep deprivation, task complexity, and time of testing differentially impact rodent cognitive performance. Recognition memory was evaluated using four versions of the Novel Object Recognition (NOR) task. Handled mice outperformed their non-handled counterparts across every combination of delay phase duration and lighting condition during testing. It also appears that memory performance on a simple NOR task is more resistant to circadian modulation and the impairing effects of sleep deprivation than on a complex NOR task. Further work is merited in order to elucidate how sleep and circadian rhythms synergistically act to afford mammalian learning, but this study underscores the importance of environmental parameters for recognition memory.
An Unexpected Challenge in Using Forward-Mode Automatic Differentiation for Low-Memory Deep Learning
Keywords:  Department of Computer Science
optimization
automatic differentiation
deep learning
machine learning
systems
Stanford University
Computer Science Department
School of Engineering
Computer Science
Honors Thesis
Text
Thesis

In this thesis, I study a simple randomized algorithm for training neural networks with extremely low memory overhead: "guess the gradient"' (GTG). I describe how to efficiently compute the directional derivative of the network's loss with respect to a randomly hypothesized gradient, and use this information to refine the hypothesis into a noisy unbiased gradient estimator that can be passed to a standard gradient descent optimizer.
Previous theoretical work has concluded that in convex settings, GTG-like algorithms suffer an O(N) slowdown for N-dimensional problems, making them impractical for large-scale deep learning. However, because the directional derivative can be computed without backpropagation, GTG can be run using very little memory. This valuable property, along with the possibility of a simple (novel, to our knowledge) variance reduction technique, encourages us to nonetheless try applying GTG in memory-bound deep learning settings.
We find that in practice GTG does not perform well on a standard deep learning optimization task — but, curiously, not for the "obvious" reason of O(N)-slower convergence. In early phases of training GTG indeed does as well as SGD with a comparable step size; however, in later phases we observe a sudden "plateauing" phenomenon that is as yet unexplained. Understanding this phenomenon could suggest a way to make GTG practical, or, failing that, shed light on the surprising effectiveness of SGD.
Analysis of quantization and normalization effects in deep neural networks
Keywords: Electrical Engineering.

There is great interest in the machine learning community to reduce Deep Neural Network (DNN) model sizes. Decreasing the memory and compute requirements expands the range of resource-constrained mobile applications where DNNs can be deployed. By far, the most popular method of compressing model size is uniform quantization. In this work, we illustrate how quantization performance was fortuitously advanced by Batch Normalization (BatchNorm), a technique originally developed to aid training convergence. This improvement is due to BatchNorm's reshaping of the network's activation distributions. Additionally, due to the limited consensus on why BatchNorm is effective, this work uses concepts from the traditional adaptive filter domain to provide insights into its dynamics and inner workings. First, we show that the convolution weight updates have natural modes whose stability and convergence speed are tied to the eigenvalues of the input autocorrelation matrices. Furthermore, our experiments demonstrate that the speed and stability benefits are distinct effects. At low learning rates, it is BatchNorm's amplification of the smallest eigenvalues that improves convergence speed. In contrast, at high learning rates, it is BatchNorm's suppression of the largest eigenvalues that ensures stability. Next, we prove that in the first training step, when normalization is needed most, BatchNorm satisfies the same optimization as Normalized Least Mean Square (NLMS), while it continues to approximate this condition in subsequent steps. The analyses provided lay the groundwork for gaining further insight into the operation of modern neural network structures using adaptive filter theory. Finally, we highlight contributions made to a real-world application of DNNs in the Smart Hospital space
Analyzing Parole Hearings with Natural Language Processing
Keywords:  Department of Symbolic Systems
Parole
NLP
Word Vectors
Lexicons
Legal NLP
Symbolic Systems
Stanford University
Thesis

The parole process is a vital component of America's criminal justice system which allows eligible candidates to be released from potentially indeterminate life sentences. In the state of California, thousands of parole hearings are conducted each year. Despite this immense volume, however, studies of the efficacy and fairness of the parole process are largely limited to close analyses of small selections of hearings. Recent advances in natural language processing have made it feasible to automatically examine vast quantities of text, but these methods are largely untested on the domain of parole hearings. On a novel dataset of roughly 35,000 parole hearings from the state of California, we perform linguistic experiments utilizing both curated lexicons and model-based predictions to characterize the language used in hearings when conditioning on a candidate's race / ethnicity and their type of legal representation. We find that these factors predict striking differences in language use by both parole commissioners and attorneys, and our results both confirm earlier studies and indicate new directions for future research. Most notably, we find across a variety of measures that privately retained attorneys are both more active in parole hearing and more likely to make use of the relevant legal precedents than their government-appointed counterparts. Our results, broadly speaking, demonstrate the promise of unsupervised methods when applied carefully to high-impact domains. However, we also note cases in which expert guidance is necessary to draw conclusions from unsupervised experiments. We argue that a combination of both approaches will be necessary to fully unlock the promise of data-driven methods.