VictorJuiz/thesis huggingface dataset:
Comparing Functional Dependences on Interventionist Counterfactual Causal Models
Keywords: Counterfactuals (Logic)
Team semantics
Causal hierarchy
Causal models
Dependence
Logic
Causation > Mathematical models
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Thesis

Formalizing causality into terms of (mathematical) logic can allow us to determine with greater precision (1) when it is appropriate to draw causal relationships, (2) whether we can express all the nuanced types of causality, and (3) how we can define the constituent parts of the machinery that drives causal reasoning, among other objectives. While work on (1) and (2) is plentiful and exemplified by Pearl’s structural causal model (SCM), the use of functional dependence logics like Vaananen’s DL based on team semantics to describe reasoning over SCMs is a relatively new tactic toward (3).
With this year’s advent of van Benthem and Baltag’s new functional dependence logic LFD, which takes a more local and modal approach to dependence relationships, I explore in this thesis the expressibility of causality in terms of SCMs in the new system of LFD, mirroring the work done using team semantics, the main result toward this end being the new logic CFD. Comparatively between the two forms of dependence logic discussed, I argue that using the more local CFD to analyze interactions between dependence and causality is more fundamental, fine-grained, and easily controllable, with respect to its global counterpart COD that is based on team semantics. However, I also show that localizing dependence in combined functional-counterfactual dependence logics still leaves various metaphysical problems for analyzing causality that lie outside the scope of said localization. Consequently, I then propose interventional analysis of sets of causal models, rather than singular models, might help solve some of these issues. All throughout, I will show how even in these localized settings, the stratification of causal inference tasks encapsulated in the Pearl Causal Hierarchy hold — and that, in general, logic still backs that correlation (almost) never implies causation.
Complexity of in-vivo DNA binding preferences of human C2H2 zinc finger transcription factors
Keywords: Physics.

The Cys2-His2 (C2H2)-Zinc Finger (ZF) transcription factors (TFs) constitute approximately half of all human TFs. These TFs contain multiple ZF DNA binding domains, giving rise to the possibility of multiple complex DNA binding motifs arising from the usage of different subsets of ZFs at different binding sites. Although they are the most abundant TF family in the human genome, the C2H2-ZF TFs also remain the most understudied. The lack of antibodies for them combined with their usage of different ZFs in different binding sites have made their in-vivo DNA binding preferences challenging to characterize fully. In this study, we fill this gap by generating genome-wide ChIP-seq profiles for 344 human C2H2-ZF TFs to complement previously published ChIP-seq and ChIP-exo datasets. We then trained neural networks (NNs) to learn high-performance sequence models of base-resolution binding profiles of 509 C2H2-ZF TFs. We discovered 100s of previously uncharacterized novel motifs. Intriguingly, several TFs exhibited complex binding preferences involving secondary motifs, motifs with variable spacing, and alternate motifs within bound Transposable Elements (TEs). Comparisons of these motifs with the commonly used bacterial one hybrid (B1H) ``Recognition Code'' revealed variable context-specific combinatorial usage of ZF domains, including frequent ``skipping'' of individual ZFs. We corroborated these findings by in-vitro characterization of protein-DNA interaction specificity using Spec-seq, HT-SELEX, and Genomic HT-SELEX. The detailed in-vitro measurements of TF-DNA interactions by Spec-seq elucidate the energetic differences across alternate binding modes. Alternative binding modes that are context-specific also reveal the biophysical mechanism behind highly targeted binding to Transposable Elements(TE) by KRAB-ZFs and give us a motif-centric window into KRAB-ZF evolution
Compressibility, variable density and curvature effects on turbulent shear layers
Keywords: Mechanical Engineering.

Turbulent mixing layers occur between two streams of fluids with different kinematic and/or thermodynamic properties, and are fundamental flow features which influence the dynamics of a wide variety of applications, ranging from the mixing efficiency of fuel injection in internal combustion to vehicle loads in external aerodynamics. Two motivating applications behind this research include the study of high-speed jets in cross-flow and supersonic retro-propulsion, which occurs as aerospace vehicles use jet plumes to decelerate during entry, descent, and landing. Both these applications are highly influenced by hot jet plumes under highly compressible conditions which exhibit significant streamwise curvature. Simulating the entire flow field associated with such applications is extremely computationally expensive; capturing all important flow features at full resolution is exorbitant for the purposes of engineering design. Thus, grasping the full behavior of turbulent mixing layers in a representative parameter space enables the development of models which can reliably and accurately predict the complex flow fields present in these engineering applications. This work enriches the present understanding of mixing in turbulent shear layers via the systematic inclusion of compressibility, variable density, and streamwise curvature effects. The spreading, or growth rate, of turbulent shear layers is known to decrease with increasing compressibility. Dilatational velocities and pressure-dilatation magnitudes show little contribution to shear layer growth rates, even under highly compressible conditions. A new turbulent length and velocity scale is introduced and shown to scale key turbulent quantities. Inclusion of freestream density variations are also known to decreasing mixing layer growth rates. Trends with increasing compressibility and the importance of mixing layer asymmetry are identified--shear layer centerlines and turbulent stresses in variable density shear layers are biased towards the less-dense freestream, which reduces the turbulent mixing of the mean momentum profile and corresponding growth rates. The combined effects of compressibility and streamwise curvature are demonstrated to be comparable for the selected parameter space. Shear layer growth rates are dominated by the freestream density ratio when streamwise curvature is significant. Changes to model predictions of turbulent growth rates and turbulent kinetic energy levels resulting from various model modifications are evaluated. Reduced accuracy in model predictions of turbulent kinetic energy magnitudes under curved conditions, even with the inclusion of compressibility and curvature modifications, is demonstrated
Compression along the visual pipeline
Keywords: Electrical Engineering.

Vision is one of our primary senses and means of obtaining information about the world. In fact, videos represent upwards of 80% of the internet traffic as of today. Imagine the journey of a video — from capturing of the visual information, through its compression, to its transmission. Once this compressed video is received and decoded, the human retina transduces the visual information into electrical signals which results in some salient perception. My thesis is dedicated to various aspects of this visual pipeline, with particular focus on compression. In this thesis, I present my research on Brain-Machine-Interfaces (BMIs) and video compression as pertaining to the visual pipeline. The first part of the thesis is dedicated to BMIs, in particular, epiretinal prostheses. First we describe a lossy compression framework, called wired-OR, to enable large scale electrical recordings for BMIs. Simulation results using data obtained from primate retina ex-vivo with a 512-channel electrode array show average compression rates of up to ~40X while missing less than 5% of the cells. Next, we provide a mechanism to avoid inadvertent stimulation-based percepts in epiretinal prostheses, called axon-bundle artifacts. We provide a simple and principled algorithm to determine axon bundle thresholds which is able to detect the onset of axon bundle activation for 88% of the electrodes within +-10% of the manually identified threshold, with a correlation coefficient of 0.95. The second part of the thesis is dedicated to video compression, in particular how we can exploit the properties of human perception for detecting characteristic video artifacts as well as doing extreme compression of videos with prior information. First we describe CAMBI, Contrast-Aware Multiscale Banding Index, to predict visibility of banding artifacts in visual content. CAMBI correlates well with subjective perception of banding, with a correlation coefficient of 0.93, while using only a few visually-motivated hyperparameters. Finally, we conclude this thesis with Txt2Vid, which enables ultra-low bitrate compression of webcam videos by utilizing state-of-the-art generative models as decoders. Txt2Vid achieves two to three orders of magnitude reduction in the bitrate as compared to the standard audio-video codecs (encoders-decoders), while maintaining equivalent Quality-of-Experience
Computational and communicative efficiency in language
Keywords: Linguistics.

As humans, we use language with ease and speed, solving the complex computational problem of processing form and meaning seemingly without effort. This dissertation studies how the properties of language enable us to achieve this, by investigating what is computationally difficult about language, and what is easy. We first investigate the principle of least effort, formalize it using contemporary machine learning methods, and argue that it can account for prominent typological patterns in word order. We then study the interplay of memory and surprisal in language processing, drawing on information-theoretic techniques to show that the order of words and morphemes efficiently trades off these aspects of complexity. Third, we investigate what makes language comprehension difficult for machines, proposing and validating a complexity metric that predicts the success of machine learning algorithms. Taken together, this dissertation introduces formal and computational techniques to precisely quantify the complexity of processing language, and to understand its implications for the structure of human language
Computational and Experimental Definition of a Consensus Ferroptosis Regulatory Network
Keywords:  Department of Biology
Biology
Ferroptosis
Genetic Regulators
Cell Death
ACSL4
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Thesis

Ferroptosis is a form of non-apoptotic regulated cell death associated with an accumulation of lipid reactive oxygen species (ROS). The genetic regulation of ferroptosis is not clearly understood. Several important regulators of ferroptosis have been identified previously, but the picture is still not complete as there are disagreements between published genetic screens about what genes are important. Here, we assemble a complete network of what is known about genetic regulators of ferroptosis. The research includes data mining and in vitro work. The data mining consisted of a literature search for all ferroptosis genetic screens, combining all of the data to construct a ferroptosis regulatory network. A clear divide in genetic regulators of ferroptosis via direct GPX4 inhibition and regulators of ferroptosis via system xc- inhibition emerged in the network. In vitro, cells were transiently transfected to reintroduce ACSL4, the most highly connected gene in the network, demonstrating that ACSL4 is required for death by GPX4 inhibition and providing a framework for future research into other highly connected genes. The network also demonstrated this genetic separation, with ACSL4 shown as a key gene required for death by GPX4 inhibitors but not by system xc- inhibitors. This work demonstrates that there is a clear separation of genetic regulators of the two pathways of death by ferroptosis and indicates the need for future work into understanding this separation.
A computational approach to criminal justice reform
Keywords: Management Science and Engineering.

In recent years, activists and policymakers have become increasingly concerned about the use of data and algorithms in criminal justice settings, fearing that their use will reinforce demographic disparities and perpetuate punitive policies. This dissertation demonstrates how the careful application of such approaches also has the potential to reduce disparities and incarceration in a series of real-world applications. I begin by reviewing a collection of analytic techniques to identify unnecessary and discriminatory police stop practices. Next, I review two new prosecutor-oriented algorithms to aid the decision to charge or dismiss a case after an arrest has occurred. I subsequently review a group of algorithms and analyses intended to reduce the use of incarceration, including risk assessment instruments, pretrial behavioral nudges, and post-prison re-entry programs. I conclude the dissertation by discussing the larger context surrounding these approaches, including some risks and limitations. Overall, my dissertation demonstrates that computational approaches are a valuable tool to advance reform in the criminal justice system
Computational methods for multi-scale biological modeling and imaging
Keywords: Electrical Engineering.

Advances of microscopic technologies and imaging instrumentation have enabled several major discoveries in biology, many of which would have been impossible without the assistance of computational techniques. On the other hand, while the resulting images carry abundance of information for human eyes, even more insights might be revealed by using advanced analysis or images-based modeling, which are made possible by computational approaches. In this dissertation, we demonstrate how computational methods can assist biological studies with multi-scale tools by presenting a series of projects that utilize computational tools to enhance, make use of, or extract information from images. We first propose a computational imaging method that is capable of combining diffraction and lens-based images to deliver images with higher resolution. We then discuss how a diffusion-reaction model can help us reveal the extraordinary capacity of nutrient acquisition of surface layers, a family of crystalline proteins that are found in many bacteria and most of archaea. Finally, we report a machine learning pipeline that can build an atomic protein model into a cryogenic electron microscopy map in a fraction of a minute. Taken together, these methods demonstrate how the interplay of imaging, modeling and machine learning can enable new discoveries for structural biology, and pave paths toward more automatic and efficient structure determination
Confident and reliable statistical predictions in changing environments
Keywords: Statistics.

A complete machine learning pipeline typically spans three distinct phases: first data collection, then model selection and training, and finally model validation, evaluation and eventual failure detection. While a large part of the statistical machine learning literature discusses the second of these tasks, the last one arguably becomes more central as the decisions we trust our statistical models with, whether it be in healthcare, transportation systems, finance or policies, turn increasingly critical, which is why in this thesis we focus on that particular aspect. We propose novel algorithms that quantity the uncertainty of predictions and construct reliable end-to-end predictive pipelines, even allowing them to leverage weaker forms of data supervision in the process. We additionally focus on a model's behavior after its release, and make sure to guarantee adequate performance even when future test distributions vary from the initial available ones; in particular, we design algorithms that are capable of detecting and localizing potential failure modes of our model, with the end goal of improving it on specific ``hard" slices of our data. Each of our methods typically builds on top of any black-box predictive model and comes with tunable and quantifiable guarantees, allowing the practitioner some flexibility when designing their model. Additionally, we present empirical results of our methodology on real-world data sets (including ImageNet, Covid-19, PovertyMap); we design experiments suggesting that in realistic scenarios our methods behave consistently with our initial expectations and hypotheses
Connecting California : agents of U.S. imperial expansion, 1783-1848
Keywords: History.

This dissertation investigates how California became part of the United States. It traces the long story of U.S. imperial expansion into Alta California from the inception of the United States in 1783 to the close of the U.S.-Mexican War in 1848. A cartographic, political, and military history, the dissertation tracks the imperial project of connecting California to the United States conducted by U.S. statesmen over multiple presidential administrations and field agent surveyors, diplomats, and military officials. U.S. field agents employed three main strategies to acquire California----mapping, diplomacy, and use of force----which eventually overlapped and sometimes worked at cross purposes. "Connecting California" offers three key findings. First, the imperial race for control of North America did not end with the American Revolution but rather continued with the United States as a new imperial power. Second, U.S. expansion was slow, uneven, and driven by agents on the ground who had governmental authority but lacked clear and timely directions for how to execute their goals. These U.S. agents traveled west by land and by sea in order to build out U.S. cartographic and diplomatic infrastructure that would facilitate U.S. commercial expansion through Pacific ports. Third, although U.S. statesmen modified their views of California between 1783 and 1848, U.S. leaders intended to prevent European empires, specifically Britain, from controlling California for geostrategic reasons. San Francisco Bay offered a rare deep-water port on the Pacific Coast that could facilitate the expansion of U.S. commerce into the Pacific Ocean. By controlling Alta California, U.S. leaders intended to cut Britain out as the middleman in the China and India trades and establish a base on the Pacific Coast for the U.S. Navy ships protecting American merchants and American whaling vessels in the Pacific Ocean. Despite the repeated, scholarly emphasis on the ideology of manifest destiny to explain U.S. imperialism, settlement for an expanding population that overcrowded Eastern cities was not the driving force of U.S. expansion into Alta California. Rather, U.S. control of California----specifically California ports including San Francisco Bay----was a means for securing Pacific trade and rivaling Britain in global commerce
Conscientizar—The Development and Enactment of Sociopolitical Consciousness and Critical Hopefulness in Salinas Youth Activists
Keywords: Youth movements > Political activity
critical consciousness
collective action
quarantine schooling
Hope
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Thesis

In March 2020, a photo of two elementary-aged girls working alone outside of a Taco Bell in Salinas, California went viral. The girls used the franchise location’s wi-fi to complete their online schooling, as a result of initial stay-at-home orders, because they did not have internet access at home. In response, the youth of Salinas rallied around them, collecting funds to provide for the girls’ internet and those of other children in similar situations. After several weeks of coverage by the media, both locally and nationally, and push from local youth who were consistently and constantly representing this clear inequality in access to basic technology at community meetings, the issue was brought up to Salinas’ school board members who created a network of hotspots in safe areas around the city for students in need to use when needed.
This honors thesis analyzes the work of Salinas youth activism during quarantine and the immediate aftermath through the lens of critical consciousness and collective action to understand the drivers pushing youth to engage in change-making collective action in an era of tremendous upheaval, uncertainty, and marginalization. Additionally, I show how youth’s formation of their critical analysis informed their demands and strategies. Drawing on 12 interviews with youth and an extended community ethnography of La Cosecha, a high school youth organizing group in Salinas, I found that youth’s activism was informed by critique engendered by participation in activism. Furthermore, I illustrate how various factors: quarantine shifts, adult actions, and group practices greatly impacted the capacity of youth to engage in collective action for change. This study contributes to the literature on the origins of motivation and hope within youth activists, as well as which organizational structures facilitate or impede youth activism.
Contemporary Indigeneity in Latin America: Sustainability and Climate Action through Entrepreneurship and Collaborative Partnerships
Keywords: Sustainability
Climate change mitigation
Indigenous peoples
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Thesis

Our planet is experiencing environmental change and loss of biodiversity at never-before-seen rates, and indigenous peoples are paying the highest price. By analyzing case studies from Peru and Mexico, this paper discusses how indigenous peoples in Latin America are leveraging entrepreneurial and collaborative methods to support sustainable action in this fight against climate change and loss of biodiversity. The paper discusses two different approaches towards sustainable climate action, ecotourism lodges and slow ecological fashion, and provides an overview of the social, economic, and environmental benefits which an effective adoption of either approach can offer, whilst aiming to distinguish between greenwashing and effective forms of sustainable climate action. It urges us to consider what can be learnt from these collaborative systems, and provides the call to action that we should learn from indigenous strategies of environmental protection, conscious consumption and development of sustainable products and be position ourselves as allies who understand the importance of indigenous autonomy and empowerment in decision-making, entrepreneurial and collaborative positions, if we are to avoid irreversible consequences for our Earth’s climate and biodiversity.
Copper and iron zeolites : their active sites and contributions to reactivity
Keywords: Chemistry.

Copper and iron exchanged zeolites have been shown to selectively convert methane to methanol at low temperatures. This one-step, selective conversion of methane to methanol is extremely difficult and further understanding is required to create materials that could perform this controlled reaction at a larger scale. This thesis presents advancements in the spectroscopic and computational characterization of these unique active sites as well as the requirements for their formation. This work builds off concepts from bioinorganic to define the contributions from the first and second coordination sphere of zeolites that contribute to this reactivity. The first part of this thesis focuses on copper active sites and describes the structural assignments of a new binuclear active site and a mononuclear active site. This enables an evaluation of the effects of nuclearity on reactivity. The second part of this thesis focuses on iron zeolites and presents the structural assignments of the alpha-Fe(II) precursor and the alpha-O active site that takes methane to methanol at room temperatures. Included are the formation of the alpha-Fe site in the zeolite lattice, the reactivity of alpha-O with benzene in the Fe-*BEA lattice, and a comparison of the reaction of alpha-O with methane in the large channel Fe-*BEA and small pore Fe-CHA zeolite lattices. The last study demonstrates that lattice topology can control the reactivity