VictorJuiz/thesis huggingface dataset:
Coloring outside the lines : community assembly in the life sciences

My dissertation is focused on studying the assembly of both ecological and social communities in the life sciences. First, I aimed to reimagine the current scientific enterprise within its own terms. By publishing scientific papers that center scientific art (Ch. 1), intergenerational mentorship (Ch. 2), and celebration of diverse lived experiences within academic science (Ch. 3), I show that science can flourish from diverse, equitable, and interdisciplinary groups. The topic of this research focuses on the assembly of microbial communities in the nectar of Diplacus aurantiacus (Ch. 1). By applying population genetics, functional genomics, and experimental evolution to this wild microbiome, I identify mechanisms that connect genetic variation to community-level processes such as priority effects (Ch. 2) and show that population-level variation can alter molecular traits associated with community assembly (Ch. 3). Second, I question the academic structure itself as the central nexus for scientific discovery. By dissolving the duality between science and art (Ch. 4), creating new science spaces that center culture and lived experience (Ch. 5-6) and reimagining entire educational ecosystems outside of "traditional" scientific venues (Ch. 7), I propose new frameworks for how science can be conducted and perhaps, what we consider science to be. This work is centered in community-centered art/science programming in the Greater Bay Area of California. In total, I hope this dissertation highlights how the life sciences can flourish when we celebrate a diversity of perspectives and approaches

Keywords: Humanities and Sciences.
Biology.
Advisor: Fukami Tadashi
Exposito-Alonso Moises
O'Connell Lauren A.
Peay Kabir
Combinatorics of integrable lattice models

Integrability theory has long been a productive area of research in mathematical physics, particularly in statistical mechanics. Originally introduced to explain the residual entropy of water ice, integrable lattice models have subsequently found numerous applications across diverse mathematical domains, including algebraic combinatorics, integrable probability, special functions, the representation theory of $-adic groups, and conformal field theory. This thesis delves into the combinatorics of integrable lattice models. The distinctive combinatorics arises from the integrability condition that is manifested in the Yang-Baxter equation. We employ the resulting combinatorial framework to establish applications in the theory of special functions and representation theory. The work presented here is organized into three distinct sections. The first part revolves around the six vertex model with integrable free fermionic weights. We utilize this model to introduce a novel family of Schur functions, which are dependent on two sets of variables and two sets of parameters. This newly presented family both generalizes and unifies diverse families of Schur functions from the literature, providing a consistent framework for studying their combinatorics. The second section continues our exploration with the six vertex model, investigating its integrability independently. We provide a complete solution to the parametrized Yang-Baxter equation within the context of this model. Our results unveil an unexpected algebraic structure within these solutions, forming a groupoid in relation to the operation that resolves the Yang-Baxter equation. The third and final section offers a concise overview of the application of an alternative lattice model, the bosonic lattice models, to the representation theory of $-adic groups. We clarify how the refined bosonic models, termed the colored bosonic lattice models, yield values of the spherical-Iwahori matrix coefficients for the general linear group over nonarchimedean local fields. We also demonstrate that these colored bosonic models satisfy the local lifting property, which allows us to establish a connection with the uncolored bosonic lattice models and provide new proofs to many known results

Keywords: Humanities and Sciences.
Mathematics.
Advisor: Bump Daniel
Chatterjee Sourav
Diaconis Persi
Hardt Andrew
Combining spectroscopic and imaging galaxy surveys for improved measurements of large-scale structure

Galaxy imaging surveys represent a promising opportunity for testing cosmological models, but suffer from significant challenges associated with galaxy redshift uncertainty. I present improved techniques for the use of spectroscopic redshifts for measurements of large-scale structure with galaxy imaging surveys. I additionally present the results of the application of these techniques to data from the Sloan Digital Sky Survey (SDSS) and the Dark Energy Survey (DES). The results of my work are photometric redshift measurements of the approximately 100 million galaxies comprising the DES Year 3 Weak Lensing Source Galaxy Catalog, constraints on parameters of the Lambda-CDM cosmological model based on the gravitational lensing and clustering of the DES Year 3 source and lens galaxy catalogs, an algorithm for propagating uncertainties that have been measured in simulations to analogous measurements on data, and measurements of the incidence of projection effects in optically detected clusters of galaxies. I argue that continued development of the methods I have worked on is a promising path toward a conclusive test of Lambda-CDM with galaxy surveys

Keywords: Humanities and Sciences.
Physics.
Advisor: Allen Steven W. (Steven Ward)
Roodman Aaron J. (Aaron Jay)
Wechsler Risa H. (Risa Heyrman)
Commodity Prices and Growth: Do Cobalt Price Shocks Fuel Night Light Expansion in the DRC?

As the production of electric vehicles (EVs) has increased globally, demand for the metals (such as cobalt and lithium) that make up the most critical parts of these vehicles has in turn risen. Although most EVs are sold in developed nations, low-income countries often host the richest resource reserves and thus are directly affected by the expansion of this market. Building on prior scholarship, I study the Democratic Republic of the Congo (DRC) and the effects that recent cobalt price shocks have had on communities surrounding cobalt mines. Utilizing recently available World Bank Light Every Night data as a proxy for income, I find substantial evidence suggesting that the cobalt price shock that occurred in 2017 resulted in persistent increases in the night light levels of mining provinces and, more specifically, the night light levels of districts and cities within these provinces.

Keywords: mining
nighttime lights
battery metals
clean energy
market expansion
Text
Thesis
Advisor: Dupas Pascaline
Comparative Analysis of the Gateway Belief Model for Policy Opinion Change in Sweden and the United States

The Gateway Belief Model (GBM) asserts that perceptions of consensus among experts about climate change are a gateway belief that directly impact public opinion about global warming and public support for climate policy. My research refines the model by proposing that the relationship between perceived scientific consensus (PSC) and climate attitudes might not be positive and significant throughout as the GBM implies. I introduce trust in scientists as a moderator of the relationship between PSC and climate opinion. I test the GBM and my extension utilizing data gathered from surveys of nationally representative, random samples in Sweden and the U.S. OLS regressions gauged associations between PSC and respondents' views on climate-related issues and tested whether PSC has greater impact among people with higher trust in scientists. Imposing a linearity assumption onto PSC led to positive and significant relationships between PSC and climate beliefs, supporting the GBM. However, relaxing the linearity assumption by assessing PSC categorically revealed a decreasing return to PSC. Trust in scientists failed to moderate the relationships between PSC, climate beliefs, and policy support, disputing the underlying mechanism of the GBM. Findings provide insights about the universality of scientific consensus message as a strategy to increase support for global warming action.

Keywords: Public Policy Program
Gateway Belief Model
Attitudinal Change
Global Warming
Climate Policy
Sweden
Comparative Analysis
Public Opinion
Consensus Messaging
Trust in Scientists
Stanford University
Humanities and Sciences
Public Policy Program
Text
Thesis
Compiling applications to reconfigurable push-memory accelerators

The slowing down of Moore's law and evolving of applications has underscored the increasing significance of domain specific architectures. Programmable domain-specific accelerators, such as coarse-grained reconfigurable arrays (CGRAs), have emerged as a promising middle-ground between efficiency and flexibility, but they have traditionally been difficult compiler targets since they use a different memory system. In contrast to general purpose compute platform, the memory hierarchies of domain-specific accelerators use push memories, which send data streams to computation kernels or to other levels in the memory hierarchy. To address the compilation challenge caused by push memories, in this thesis, we have introduced a novel abstraction, namely, the unified buffer, designed to support the application compilation process to reconfigurable accelerators as well as facilitating physical hardware design for reconfigurable architecture. The unified buffer abstraction enables the compiler to separate generic application scheduling optimizations from the mapping to specific memory implementations in the backend. This approach automates push memory scheduling optimization through a collection of compiler techniques, including polyhedral analysis and software pipelining, effectively shielding users from the low-level hardware details. This separation also allows our compiler to bridge the gap between resource-agnostic application description and resource-constrained hardware implementation, mapping applications to different CGRA memory designs, including some with a ready-valid interface. Furthermore, the separation also opens the opportunity for optimizing push memory elements on reconfigurable arrays. Our optimized memory implementation, the Physical Unified Buffer (PUB), uses a wide-fetch, single-port SRAM macro is 18% smaller and consumes 31% less energy than a physical buffer implementation using a dual-port memory. Finally, our system evaluation shows that enabling a compiler to support CGRAs leads to performance and energy benefits. Over a wide range of image processing and machine learning applications, our CGRA achieves 4.7x better runtime and 3.5x better energy-efficiency compared to an FPGA.

Keywords: Engineering.
Electrical Engineering.
Advisor: Horowitz Mark (Mark Alan)
Kjolstad Fredrik
Raina Priyanka (Assistant Professor of Electrical Engineering)
Compiling deep learning kernels to locality-aware dataflow

Emerging deep learning applications require unprecedented computation and memory capacity. To accelerate these applications, novel processing systems such as dataflow accelerators strive to exploit multiple dimensions of parallelism within deep learning models, e.g., tensor and pipeline parallelism. Although these systems provide ultra-high performance when fully utilized, compiling deep learning applications to harness their computation capability remains a challenging problem. With recent advances in domain-specific programming language, accelerator design, and machine learning, we now have the potential to better serve the needs of training and evaluating large deep learning applications on dataflow accelerators through algorithm, software, and hardware co-design. In this dissertation, I present the design and development of efficient deep learning optimizations and programming frameworks. I present two frameworks: SpatialRNN for accelerating recurrent neural network language models on spatial accelerators and Sigma for expressing and accelerating high-data-reuse deep learning kernels using reconfigurable dataflow accelerators. Our end-to-end evaluation using Sigma demonstrates a 5.4x speedup on kernels encompassing financial applications, traditional machine learning, language modeling and computer vision tasks over an Nvidia V100 GPU accelerator

Keywords: Engineering.
Electrical Engineering.
Advisor: Olukotun Oyekunle Ayinde
Raina Priyanka (Assistant Professor of Electrical Engineering)
Ré Christopher
Compiling image processing and machine learning applications to reconfigurable accelerators

Recent vision applications provide exciting new opportunities in photography, autonomous driving, and image generation. In turn, new hardware platforms have similarly risen to efficiently execute this class of applications. Effective compilers are necessary to seamlessly run these new applications on hardware accelerators. However, hardware accelerators use specialized memories to increase efficiency, which makes them challenging compilation targets. Addressing this issue requires changing the abstraction that the compiler uses to represent memories. In this dissertation, we present such a compiler. It compiles image processing and machine learning applications to dataflow accelerators. To create this system, we extend the Halide domain-specific language (DSL) to target streaming accelerators. Using new scheduling primitives, the user has full control over optimization decisions. These optimizations can be tailored to new hardware accelerators. We introduce a unified buffer abstraction to provide an interface between application definition and hardware memory configuration. This abstraction enables efficient hardware implementations while supporting the generality of applications that are represented in our abstraction. Our compiler enables compute sharing by generating designs that time-multiplex compute operations with low utilization. We demonstrate the effectiveness of this compiler by running applications on the Amber Coarse-Grained Reconfigurable Array (CGRA), designed by the Agile HArdware (AHA) group at Stanford.

Keywords: Engineering.
Electrical Engineering.
Advisor: Horowitz Mark (Mark Alan)
Kjolstad Fredrik
Kozyrakis Christoforos
Composable optimization for robotics simulation and control

Optimization is a fundamental part of robotics and can be seen in various aspects of the field, such as control and simulation. Both of these areas involve finding the best solutions to various optimization problems to achieve desired outcomes. Efficiency is key when it comes to solving these optimization problems. By finding solutions quickly and reliably, we can execute optimization-based controllers in real-time on hardware. The ability to quickly generate large amounts of simulation data is also valuable for offline optimization tasks such as policy optimization, co-design optimization, and system identification. Oftentimes, the optimization problems arising in robotics control and simulation have structure. Some problems directly fit into well-studied categories, for instance, the Linear Quadratic Regulator (LQR), other control problems can be cast as Linear Programs (LP), or Quadratic Programs (QP). For each of these categories there exist efficient and reliable solvers. Fitting your problem into one of these categories is often a safe strategy. However, there exist control and simulation tasks that involve complex optimization problems that do not fit these categories and for which there are currently no satisfactory solvers. In this dissertation, we focus on such problems. We are particularly interested in coupled optimization problems where the solution of one optimization problem is a parameter of another one. These coupled optimization problems can naturally arise in robotic simulation. For instance, the simulation of contact physics requires solving the least action principle and the maximum dissipation principle. We can solve these two optimization problems jointly. Coupled optimization problems also frequently arise in autonomous driving scenarios where agents are interacting. Indeed, each vehicle or pedestrian in the scene is optimizing its path to rally its destination as fast as possible while avoiding collisions. Conversely, we can deliberately choose to decompose a single complex optimization problem into a set of coupled optimization problems. Problem decomposition is a strategy that can yield significant benefits in terms of the speed and reliability of the solver. In this context, optimization problems exchange gradient information by leveraging differentiable optimization. The strategy behind these choices is what we call Composable Optimization. In this dissertation, we focus on a few applications in robotics control and simulation namely game-theoretic control, control through contact, physics simulation, and collision detection. For these problems, we leverage composable optimization to exploit problem structure and devise efficient solvers. In some cases, we may combine multiple problems into a single optimization problem, while in other cases we may decompose the problems into simpler chunks. This approach allows us to tackle more complex optimization problems in a structured and efficient manner

Keywords: Engineering.
Mechanical Engineering.
Advisor: Manchester Zachary
Schwager Mac
Kennedy Monroe
Composition and computation in dynamical systems

In this thesis, we explore two ways of analyzing complex systems. The first is compositionally --- understanding large systems as composed of smaller modules --- using tools from applied category theory. And the second is via abstraction --- understanding how continuous dynamical systems can be interpreted as discrete ones --- with applications in computational neuroscience.

Keywords: Humanities and Sciences.
Mathematics.
Advisor: Carlsson G. (Gunnar)
Vondrak Ján (Mathematician)
Vakil Ravi
Compositional representation learning : from reasoning to synthesis

The world we live in is inherently compositional: Just like a sentence is built upon phrases and words, a visual scene comprises a collection of interacting objects and entities, which in turn are derived from the sum of their parts. This compositionality plays a pivotal role in our ability to understand the world, organize the acquired knowledge through a rich set of concepts, and readily adapt them to novel situations and environments. Essentially, it is considered one of the fundamental building blocks of human intelligence. What does compositionality mean in the context of machine intelligence? How can we encourage neural networks to develop structured understanding of our surroundings? And how can we apply this knowledge to improve in downstream tasks of vision and language? These are the key questions that are explored in the dissertation. We discuss manners and mechanisms to encourage multimodal neural networks to learn compositional scene representations, and in turn operate over them in a compositional manner, which we leverage for two downstream goals: multimodal reasoning, where we introduce models that can draw a sequence of inferences about visual scenes so to answer textual questions about them; and visual synthesis, where a model can inversely generate pictures depicting multi-object scenes from scratch. We fulfill these aims by incorporating a graphical structure into neural networks, which consists of nodes and edges, respectively meant to capture the objects within the scene and the relations among them. We demonstrate how these graph-based structural priors endow neural networks with multiple desirable properties, including: data efficiency, achieved by decomposing a given task into a series of subtasks, each of which could be learned more easily; generalization, where a model can recombine known concepts together in novel ways; controllability, where modifying respective components of the model's latent representation can selectively induce intended modifications within its output; and interpretability of the computational process the model follows, either to create an image or to reason over it. Throughout this work, we study the interplay and analogies between synthesis and reasoning, and show how, with the right inductive biases incorporated, the former capability could foster the latter.

Keywords: Engineering.
Advisor: Bernstein Michael S.
McClelland James L.
Guibas Leonidas J.
Leskovec Jurij
Liang Percy
A compression-enabled approach to analyze seizures for people with refractory epilepsy

This dissertation presents an information theoretic measure called the inverse compression ratio (ICR)—an unbiased estimate of joint entropy (measure of signal complexity)—and demonstrates its potential for seizure detection. First, a minimally disruptive, scalable platform to acquire 10 kHz research-quality, intracranial EEG (iEEG) in a hospital setting was deployed to build the Brain Interfacing Laboratory iEEG repository. Second, ICR was implemented using the DEFLATE algorithm (a time independent and lossless encoder), and it outperformed other conventional, model-free algorithms. Third, leveraging video compressors, it was determined that some degrees of time dependence and lossiness in the compression encoding can improve ICR performance for seizure detection. Finally, when quantifying seizure detection performance, positive-centric performance metrics—those that ignore true negatives (e.g., area under the precision-recall curve)—were found to be more informative than others. Compression-enabled measures, without linear assumptions or parametric modeling, provide a promising direction for qEEG analyses

Keywords: Engineering.
Electrical Engineering.
Advisor: Nuyujukian Paul Herag
Nishimura Dwight George
Weissman Tsachy
Computation of wall-bounded flows using a new universal velocity profile

Turbulence has applications in countless engineering, energy and natural systems. As such, it is crucial to be able to understand and predict how flows with turbulence behave as this could lead to enhanced understanding of applications such as atmospheric circulation patterns, blood flow, pollution dispersion in the atmosphere and ocean, combustion and vehicle drag to list only a few. At high Reynolds numbers, solutions of turbulent flows, especially those that are bounded by a wall, remain a computational challenge due to the range of scales that need to be accounted for. As such, it is common to use models to alleviate the need to account for the full range of scales near the wall and help bring about wall-bounded turbulent computational solutions at a much more feasible cost. This work focuses on a new wall model that can be used in both the wall and wake layers of a boundary layer. A boundary layer develops over a wall due to the no-slip at the wall. The wall layer of a turbulent boundary layer includes the viscous sublayer, buffer layer and logarithmic layer. This near-wall region is universal to all smooth wall turbulent flows and is where the most dissipative scales of turbulence lie. The wake layer, however, is where the flow is governed far more by the overall forces that govern the flow and is extremely flow dependent. To date, there is no single theory that unifies all these regions. We seek to provide such a theory with the universal velocity profile. The universal velocity profile is uniformly valid from the wall to the edge of a boundary layer. We use direct numerical simulations, large eddy simulations and experimental data to fit it to channel flow and boundary layers with favorable, zero and adverse pressure gradients and provide a new friction law and a shape function that can be used for high Reynolds number computations. The main focus of the universal velocity profile in this work is as a computational wall model in large eddy simulations. Since it is valid in the wake layer, it can be used freely throughout a boundary layer with assurance that it will give an accurate flow prediction. Large eddy simulations for channel flow with and without a periodic hill and external flow around an airfoil are shown and the universal velocity profile is shown to be robust in its modeling capability.

Keywords: Engineering.
Aeronautics and Astronautics.
Advisor: Alonso Juan
Cantwell Brian
Lele Sanjiva