VictorJuiz/thesis huggingface dataset:
Collective dynamics of high brightness electron beam compression and manipulation

High brightness electron beams have revolutionized accelerator science and enabled a large range of high impact applications such as X-ray free electron lasers(XFEL), inverse Compton-scattering, plasma wakefield accelerators and future linear colliders. In pursue of higher beam brightness, collective effects become key limiting factors. Those collective effects happen during the beam generation, acceleration, compression and transport, including coherent synchrotron radiation, space charge and resistive wall wakefields. As people are entering the new parameter regime — such as the generation of ultra-short electron bunches or ultra-high peak current — collective effects can play a dominant role and classical theories of wakefield can break down. In this work we describe our efforts in understanding and controlling the collective dynamics of high-brightness electron beams. In the first half of the thesis, we focus on one of the most challenging beam dynamics problems in accelerator physics — coherent synchrotron radiation (CSR) and introduce a novel computational model of 2D/3D CSR in relativistic beams. The model achieves both accurate and efficient simulations of CSR in the regime that conventional 1D models become invalid, which can be crucial for the design of next generation accelerators. In the second half, we switch gear to techniques that enable better control of the collective effects. Specifically we examine laser shaping techniques both in spatial and temporal domains as a powerful beam manipulation tool. We first discuss in detail the numerical and experimental studies using Laguerre-Gaussian mode laser in the laser heater to improve the microbunching instability suppression in XFEL drivers. Then we introduce temporal laser shaping to achieve flexible longitudinal phase space manipulation and apply it as an active out-coupling method for cavity-based free-electron lasers

Keywords: Applied Physics.
Advisor: Huang Zhirong
Marinelli Agostino 
Raubenheimer Tor O.
College Student Elasticity: The Impact of Job Openings on College Major Decisions

Over time, there has been a shift in the demand for different college majors and skills, particularly engineering and science majors. Understanding how college students decide their major is important for future employment outcomes. This paper studies the effect of shifts in job opening availability on college major choice. I use a novel job openings dataset and a record of degrees conferred in the United States from 2007 to 2019. I use lags and fixed effects to estimate exposure to change in job openings by region and identify the effect on college major choice. I find that a 25 percentage point increase in job openings for a given industry leads to a 1 percentage point increase in the fraction of degrees in that industry. The results are similar for public colleges and community colleges. Students who attend small colleges and majority undergraduate colleges are more responsive to changes in job openings. In addition, students in the fields of business and humanities are more responsive to changes in job openings than other industries.

Keywords:  Department of Economics
human capital investments
higher education
field of study
labor market information
vacancy data
expected demand
postsecondary school
mismatch employment
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Thesis
Advisor: Pistaferri Luigi
College Students Aren’t As Liberal As You Think: Evidence for Political Moderation Within Homogeneous Groups

As America grows ever more polarized, a question arises: are productive discussions even possible anymore? Due to the rising phenomenon of partisan sorting, more and more Americans are living in enclaves that share not just demographic features but are politically homogeneous (Brown and Enos 2021). A set of famous findings in the literature offers little hope: the widely-accepted universal law of group polarization states that homogeneous groups always become more extreme after discussion (Sunstein 2002). Groups that enter a conversation leaning towards one perspective come out ever more ensconced in opinions more radical than previously held (Friedkin 1999). If this law really is universal, it poses a serious problem for the efficacy of political conversations in the United States. This thesis aims to disprove the universality of this law by considering a study, “Shaping Our Future” from the Stanford Center for Deliberative Democracy, undertaken with a group of highly homogeneous college students (n = 541, where 75% of the sample identified as or leaned towards the Democratic Party, compared to just 11.5% for the Republican Party). College students are a perfect population for this question as they tend to be highly liberal and are the subject of a current political battleground between the left and the right. By examining opinion change using a pre- and post-study survey method, this thesis demonstrates that this homogeneous group moderated (decrease in ideological polarization) on a majority of proposals as well as grew warmer (decrease in affective polarization) towards the other side. In particular, this group of liberal college students became more conservative on economic issues. T-tests are used to prove statistical significance. Overall, I find that this group, despite its makeup, did meaningfully reduce polarization through discussion. I therefore offer a potential counterexample to the universal law of group polarization as well as a hopeful note for future conversations in the college campus climate and around the nation.

Keywords: Polarization (Social sciences)
Politics and government
Political parties
ideological polarization
group polarization
Psychology
symbolic systems
affective polarization
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Thesis
Advisor: Siu Alice
Diamond Larry
Combating noise and uncertainty in biophysical models

Noise and uncertainty are ubiquitous in biological systems, and robustness to these effects may be a crucial piece of understanding biological design. In small biophysical systems, thermal fluctuations can be of the order of the energy difference between system states, and these fluctuations are an important operational consideration for systems at the mesoscale. At the behavioral level, organisms constantly confront an unpredictable world and must make decisions that achieve their goals but are also sensitive to risk. In this work we discuss two projects that attempt to provide insight into robust biophysical models at two very different scales. At the smallest scale, we investigate theoretical bounds on the accuracy of single cellular sensors and how this is limited by energy dissipation. We then move to the behavioral scale and apply large deviation theory to risk-sensitive reinforcement learning in order to generate variance-constrained policies

Keywords: Applied Physics.
Advisor: Ganguli Surya
Fisher Daniel S.
Good Benjamin H.
Combining electroanalysis and nanoscale characterization to understand battery electrolytes

Development of batteries with higher energy density than conventional Li-ion batteries can facilitate electrification of passenger vehicles, aeronautics, and long-range transportation. Metallic lithium is the ideal anode for maximizing energy density, but insufficient rechargeability limits its widespread adoption. The reactivity of Li with electrolytes and growth of filamentary "dendritic" Li morphologies during charging underly its poor rechargeability. Electrolyte engineering can simply and effectively mitigate these problems; however, the mechanistic basis for the dependence of Li passivation and morphology on electrolyte chemistry remains poorly understood. In this dissertation, I discuss how it is now possible to understand the complex multiscale relationship between electrolyte chemistry and rechargeability by using the complementary strengths of electroanalysis and cryogenic transmission electron microscopy. First, I discuss the electrochemical and structural evolution of a passivation film on Li called the solid electrolyte interphase (SEI) in select electrolytes. The results refine our understanding of SEI structure and growth and suggest control of electroplated Li morphology is key to prevent corrosion of Li during calendar aging. Next, I will discuss the current and electrolyte dependence of Li plating pathways and kinetics to reveal the main factors controlling Li morphology. The results first show that fracture of the SEI and exposure of fresh Li-electrolyte interfaces -- as opposed to mass transport limitations alone -- are responsible for dendritic Li plating during fast charging. I then discuss a new electroanalytical method using ultramicroelectrodes that can probe the charge-transfer kinetics at fresh Li-electrolyte interfaces after SEI fracture. The kinetics deviate from classical Butler-Volmer models and provide insight into how solvation and interfacial structure tune reaction rates in batteries. Correlating the kinetics in many electrolytes to rechargeability suggests that the equilibrium potential of Li/Li+ redox and surface energy -- thermodynamic factors modulated by the strength of Li+ solvation -- underly trends of Li morphology across distinct classes of electrolyte

Keywords: Chemistry.
Advisor: Cui Yi
Chidsey Christopher E. D. (Christopher Elisha Dunn)
Kanan Matthew William
A compact reader for wireless passive sensors

The demand for continuous, long-term wireless sensing is growing in applications where tethered links cannot be used. Achieving a complete sensing solution requires innovations both in the reader and the sensor to make them easy-to-manufacture, small, safe, disposable, and yet sufficiently functional. Fully passive sensors (FPS) which consist of only a resistive-capacitive-inductive (RLC) tank are widely used as a simple and inexpensive alternative to their active counterparts. Although great effort and progress have been made in developing such sensors, the reader design remains a bottleneck. Existing solutions rely on forced excitation which makes the implementation inherently more complex. Self-oscillation circumvents these limitations. We show that a nonlinear negative resistance which exhibits a step-like current-voltage characteristic enables self-oscillation to automatically find and lock to the sensor value. Moreover, use of a non-resonant reader coil or unidentical tank topologies in the reader and sensor results in distance dependency of measurement results. We investigate the sources of distance variations and suppress their effects by optimizing the tank topology and employing a closed-loop mechanism which locks to the point of operation with minimum variation. Both improvements allow for a simple reader sticker (5.4cm by 3.4cm) which is wirelessly powered using near-field communication signals and achieves < 0.16% variation over a distance of 1.6cm for a 1.2kOhm sensor resistance. This simple reader solution advances the field of continuous, ubiquitous, and long-term sensing, paving the way for widespread deployment in daily applications, such as food safety, structural, and health monitoring

Keywords: Electrical Engineering.
Advisor: Poon Ada Shuk Yan
Emami-Naeini Abbas
Howe Roger Thomas
Comparing Functional Dependences on Interventionist Counterfactual Causal Models

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.

Keywords: Counterfactuals (Logic)
Team semantics
Causal hierarchy
Causal models
Dependence
Logic
Causation > Mathematical models
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Thesis
Advisor: Icard Thomas
van Benthem Johan
Complexity of in-vivo DNA binding preferences of human C2H2 zinc finger transcription factors

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

Keywords: Physics.
Advisor: Doniach S.
Kundaje Anshul
Fordyce Polly
Greenleaf William James
Compressibility, variable density and curvature effects on turbulent shear layers

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

Keywords: Mechanical Engineering.
Advisor: Lele Sanjiva K. (Sanjiva Keshava)
Alonso Juan José
Mani Ali (Professor of mechanical engineering)
Moin Parviz
Compression along the visual pipeline

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

Keywords: Electrical Engineering.
Advisor: Weissman Tsachy
Murmann Boris
Sra Misha
Computational and communicative efficiency in language

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

Keywords: Linguistics.
Advisor: Degen Judith
Jurafsky Dan
Frank Michael C. (Professor of human biology)
Computational and Experimental Definition of a Consensus Ferroptosis Regulatory Network

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.

Keywords:  Department of Biology
Biology
Ferroptosis
Genetic Regulators
Cell Death
ACSL4
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Thesis
Advisor: Dixon Scott J.
Morrison Ashby J.