VictorJuiz/thesis huggingface dataset:
KRAS-dependent intercellular signaling organizes the tumor microenvironment
Keywords: Humanities and Sciences.
Biology.

Cancer is amongst the leading cause of death in the world, with lung cancer being the leading cause of cancer related deaths. Over half a century of cancer research has revealed many key features of cancer, yet for many cancers we are no closer to treating cancer than the 1900s. Part of this has to do with the heterogeneity of tumors, where the genomic instability induces mutations causing heterogeneous population of cancer cells within one tumor. This would not be much of a problem if this was the only source of heterogeneity in a tumor, but research into the tumor microenvironment reveal the tumor landscape adds several degrees of complexity to tumors. The tumor microenvironment has rapidly gained attention in the cancer community, quickly moving to the forefront of cancer research. The intercellular signaling between cancer cells and its surrounding cells dictate microenvironment composition and tumor progression. Over a dozen different cell types are found in the tumor microenvironment and communicate with each other to create a stable environment for cancer. Much of this communication happens through signaling proteins secreted to the extracellular space. These secreted proteins have already been implicated in various tumor related functions such as tumor growth, angiogenesis, immunosuppression, and metastasis. Despite their importance, the composition of secreted protein in the tumor microenvironment and much of the secreted protein's function remain unknown. This is partly due to the difficulty of studying this set of protein as they are dispersed outside the cell. We expanded the understanding of cancer secreted proteins by profiling the secretome of NSCLC driven by the most prolific oncogene, KRAS, using data independent acquisition on Bruker's state-of-the-art mass spectrometer timsTOF Pro. We identify over 1000 proteins secreted by 27 NSCLC, of which 76 were significantly changed in response to KRAS inhibition. We produced these proteins and applied them to cells in various functional assays to characterize their function and reveal proteins involved in key tumor functions cell including proliferation and cell migration. We then examined signaling responses in the recipient cells by measuring global kinase activity changes in the responding cells using phosphoproteomic analysis and discovered the casein kinase 2 was upregulated in metastasis in response to cancer secreted proteins. We hope to apply our research to translational research, using combinatory treatment based off various points of vulnerability found in our study as therapy for cancer with hopes that we can eradicate this disease once and for all.
Language, multilingualism, and power in world language teachers' ideologies about a second language education
Keywords: Education.

This work questions what is meant by a "multilingual education" in the United States, including how its understandings may be perpetuating harmful power asymmetries. Drawing on interview data from high school world language teachers, it utilizes inductive coding of transcripts to surface teachers' ideologies--professed beliefs, thoughts, and feelings--about a second language education, as well as their thoughts on multilingualism and conceptions of language. Findings include six ideologies, two views of multilingualism, and five conceptions of language, leading to a discussion of two overall interpretations of language and its education: proficiency and expansiveness. The former is argued to be a monolingual view of multilingualism, and implications are considered in light of its ironic and problematic prevalence.
Large detectors for rare event searches
Keywords: Humanities and Sciences.
Physics.

In the last few decades, neutrinos proved to be an extremely rich field to explore physics beyond the Standard Model (SM). In particular, neutrino oscillations are the first substantial phenomenon not predicted by this current framework and represent a major success in this field. Nevertheless, our knowledge on this elusive particle is still lacking of fundamental pieces, such as its mass and nature, which could explain, for example, the origin of the matter/antimatter asymmetry we observe today. Neutrinoless double beta decay (0νββ) is a nuclear process that, if observed, could answer these crucial open questions. These increasingly challenging measurements require dedicated detectors specifically designed for the ultimate physics goal. In this thesis, I'll describe how the physics reach constrains the detector design. To do this, I'll focus on two projects I worked on: the first one is an exploratory study of a novel concept of liquid scintillator detector employing segmented optics, while the second is the design and characterization of two instrumentation setups deployed in liquid xenon. The latter is part of the R&D effort of nEXO, a tonne-scale time projection chamber filled with 90% enriched 136Xe aimed to improve the sensitivity to 0νββ
Large powers of random matrices
Keywords: Humanities and Sciences.
Mathematics.

This dissertation comprises five papers, concerned with Wishart and Wigner ensembles. The underlying technique for its results is the method of moments, which in random matrix theory amounts to computing expectations of traces of large powers of matrices and inferring from them properties of the eigenspectrum. The main novelties are twofold, employing trace differences, and using traces to obtain eigenvector universality for a broad family of distributions. This combinatorial approach contrasts with the prevalent means for tackling questions related to eigenspaces, local laws, which rely on resolvents and stochastic differential equations
The large-misalignment mechanism for the formation of compact axion structures
Keywords: Humanities and Sciences.
Physics.

Axions are some of the best motivated particles beyond the Standard Model. We show how the attractive self-interactions of dark matter (DM) axions over a broad range of masses, from 10^-22 eV to 10^7 GeV, can lead to nongravitational growth of density fluctuations and the formation of bound objects. This structure formation enhancement is driven by parametric resonance when the initial field misalignment is large, and it affects axion density perturbations on length scales of order the Hubble horizon when the axion field starts oscillating, deep inside the radiation-dominated era. This effect can turn an otherwise nearly scale-invariant spectrum of adiabatic perturbations into one that has a spike at the aforementioned scales, producing objects ranging from dense DM halos to scalar-field configurations such as solitons and oscillons. We call this class of cosmological scenarios for axion DM production "the large-misalignment mechanism." We explore observational consequences of this mechanism for axions with masses up to 10 eV. For axions heavier than 10^-5 eV, the compact axion halos are numerous enough to significantly impact Earth-bound direct detection experiments, yielding intermittent but coherent signals with repetition rates exceeding one per decade and crossing times less than a day. These episodic increases in the axion density and kinematic coherence suggest new approaches for axion DM searches, including for the QCD axion. Dense structures made up of axions from 10^-22 eV to 10^-5 eV are detectable through gravitational lensing searches, and their gravitational interactions can also perturb baryonic structures and alter star formation. At very high misalignment amplitudes, the axion field can undergo self-interaction-induced implosions long before matter-radiation equality, producing potentially-detectable low-frequency stochastic gravitational waves.
Large-scale and continuous subsurface monitoring using distributed acoustic sensing in urban environments
Keywords: Sustainability.
Geophysics.

This thesis investigates using Distributed Acoustic Sensing (DAS) as a scalable and cost-effective approach for continuous, large-scale monitoring in urban settings. DAS capitalizes on existing telecommunication infrastructure to establish high-resolution seismic recording arrays. The research specifically targets two critical domains: traffic management and near-surface monitoring. The central innovation of this work lies in leveraging moving vehicles as cost-efficient, non-specialized indicators for both traffic and seismic monitoring. This involves tackling various obstacles such as pinpointing the geographical positions of DAS channels, improving the clarity of traffic signals, and overcoming the processing constraints and diminished resolution typical of conventional ambient noise interferometry methods. I first conduct comprehensive field experiments and simulations to characterize vehicle-induced seismic signals, thus providing foundational knowledge for more sophisticated monitoring techniques. Subsequent chapters introduce scalable and precise mapping methods that utilize concurrent onboard GPS and DAS recordings. These methods have proven essential for tasks like fiber interruption localization, enhancing vehicle tracking accuracy, and near-surface characterization. To tackle the issue of degraded signal resolution in urban DAS applications for traffic monitoring, an advanced machine learning algorithm is developed, yielding improved vehicle detection and tracking. Finally, to overcome the limitations of conventional ambient noise interferometry, I present a targeted interferometry approach based on the Kalman filter algorithm. This method enables high-resolution, cost-effective, and time-sensitive characterizations of the near-surface environment, effectively capturing phenomena such as rainfall-induced soil saturation changes. In summary, this research signifies a substantial advancement in the application of DAS technology for urban monitoring, offering solutions that are both state-of-the-art and economically sustainable.
Large-scale measurements of the cosmic microwave background
Keywords: Humanities and Sciences.
Physics.

The cosmic microwave background (CMB), the afterglow of the Big Bang, is a cornerstone of modern cosmology. Measuring its features with ever-increasing detail has been key to building and validating our current standard cosmological model, thus motivating further attempts to study the CMB with ever greater precision. First, we present the development of the microwave SQUID multiplexer. We show the design and validation of custom warm readout electronics and discuss studies of the fundamental properties of the device and readout combined system. We discuss the first on-sky CMB demonstration of umux and production-scale development at 910x multiplexing factor for upcoming CMB observatories. Together these efforts increase the achieved multiplexing factor for deployment-grade CMB detector systems by over an order of magnitude. Second, we present measurements of CMB E-mode polarization at large angular scales with the BICEP/Keck program. We show the design and execution of new extended-area observations with BICEP3 to map large-scale E-modes across almost 20\% of the sky. We present the updated analysis pipeline for mapmaking, curved sky power spectrum estimation, and likelihood framework. Preliminary analysis shows sensitivity to new cosmology comparable to the best satellite observation-based constraints, demonstrating the potential of future ground-based surveys. Finally, we live in privileged times in cosmology, with access to unprecedented instruments and computing power. This work cannot be done alone: large collaborations of hundreds of scientists are responsible for producing much of the stunning success of modern cosmology. We present thoughts on structural policies to ensure the diversity, equity, and inclusivity of these large-scale collaborations moving forward
Laser modification of nuclear fusion cross-sections
Keywords: Engineering.

The effect of ultraintense, x-ray laser fields on the value of the nuclear fusion cross-section is studied. Analytic models of this theoretical effect are explored alongside a direct numerical approach to its governing quantum-mechanical equation of motion. Consistencies between certain models and the numerical solution suggest a simplified physical picture sharing features with atomic ionization in optical laser fields, as well as with quantum phase-modulation problems. An estimate of the laser-modified thermonuclear reaction rate in an idealized, low-temperature deuterium-tritium plasma is calculated. Results describe strong-field enhancement effects taking hold at field strengths that are of considerable magnitude, but below the Schwinger limit (taken as an upper cutoff)
Laser path optimization strategies for laser powder bed fusion
Keywords: Engineering.
Mechanical Engineering.

Metal additive manufacturing promises to enable the production of complex geometries with potentially exotic material properties. One leading paradigm of metal additive manufacturing is laser powder bed fusion, where a laser shines on a powder bed to selectively fuse powder together via heating, layer by layer, to build up a part. The path of the laser strongly affects the temperature field history of the part, which in turn determines the part's microstructure, as well as the presence of defects like porosity, warping, and residual stresses/strains. With the goal of elucidating how good or optimal laser paths can be identified, this thesis presents three main contributions. After an introduction and review of existing laser path optimization strategies in Chapter 1, Chapter 2 demonstrates the viability of modeling the optimal control problem as a board game and applying deep reinforcement learning a la AlphaGo Zero (albeit with major modifications) as an optimization technique. Then in Chapter 3, we show that by making the assumption that the effect of laser heating is finite in time, our optimal control problem can be modeled as an Traveling Salesperson Problem with History, which we prove can be transformed into equivalent Equality-Generalized Traveling Salesperson Problems and Traveling Salesperson Problems. Chapter 4 demonstrates how the structure of the Traveling Salesperson Problem with History can be used to develop a dynamic programming algorithm for solving and how it affects different solvers' performances. Finally, this thesis concludes with reflections on the potential and limitations of our work to improve laser powder bed fusion beyond computational models
The last Black public high school in Oakland : what is at stake with the relationship between school closures and Black administered and attended schools
Keywords: Education.

While research has documented the more tangible causes and consequences of school closures in majority-Black neighborhoods, little explores the equally significant consequences of fear of school dissolution and closure on Black children, teachers, and school communities, as caused by Black physical and cultural displacement. This dissertation argues that public non-charter schools, where Black children are educated and Black educators labor, operate under persistent threat of closure and, beyond that threat, are indeed the schools into the 21st century most likely to be shuttered. This phenomenon of school dissolution is placed in conversation with large structural scales of displacement through a novel longitudinal nationwide quantitative study which examines the likelihood that majority Black schools are closed as compared to their non-majority Black counterparts. Further, thematic analysis and ethnographic research methods were utilized to explore one school district's disappearing African American community, the impact on its last majority-Black public high school, and the developed narratives about why the school has been drastically under enrolled - ultimately, threatening its long-term viability. Finally, this dissertation challenges the notion that Black administered and attended (BAA) schools, especially those operating under the threat of closure, do not offer exceptional instruction via interrogating how the conditions of Black displacement shape and inform a veteran African American teacher's pedagogical choices
Learned motion models for the perception and generation of dynamic humans and objects
Keywords: Engineering.

Understanding the motion of humans and objects is key for intelligent systems. Motions are the result of physics, but non-physical dynamics also play an important role, for example, social norms and traffic laws determine how pedestrians and vehicles behave. The ability to perceive and generate these motions enables important applications, such as autonomous robots that operate in the real world, mixed reality that augments the real world, and animation and simulation that imitate the real world. Despite often being approached as separate problems, the perception and generation of motion both fundamentally rely on having an accurate model of dynamics for humans and objects in a scene. Perception problems like pose estimation, tracking, and shape estimation require motion understanding to reason about occlusions and noise from partial and ambiguous inputs. Generation problems such as forecasting future motion rely entirely on being able to predict motion. A promising avenue to solve these problems is learning models of motion, however, it is challenging to develop models that accurately reflect the real world, capture the diversity of motion due to inherent uncertainty, and robustly generalize to many possible scenarios. This thesis explores how to effectively learn models of motion to solve important perception and generation problems. We propose several data-driven methods to accurately capture the dynamics of humans, objects, and how they interact with each other and their environment. In the first part of the thesis, we introduce two methods for perceiving 3D human pose and 3D object shape, respectively. The first uses a robust generative model of 3D human pose transitions, while the second learns a continuous motion representation entirely from point cloud observations. The second part of the thesis focuses on motion models for synthesizing high-level human behavior in the form of 2D top-down trajectories. In these works, we introduce two new generative models that handle complex multi-agent interactions and can be controlled by a user to produce trajectories with desirable properties. We show this is useful to create rare scenarios for testing autonomous vehicles and to animate crowds of pedestrians. Finally, the thesis ends with a discussion of important future directions to continue improving learned models of motion for humans and objects
Learned Syntax Aware Embeddings Using Equivariant Graph Neural Networks
Keywords: Embeddings
graph neural networks
Natural language processing (Computer science)
Biochemical engineering > Computer simulation
Text
Thesis

In this paper, we aim to improve foundational model embeddings by explicitly leveraging syntactic information. Present foundational models do not explicitly model structural information, which can be problematic in certain domains such as biochemistry. We model this syntactic information as a graph to auto-encode domain-specific information into the large model embeddings. Particularly, we implement an Equivariant GNN version of the SIWR model to further improve downstream performance by considering a given sequence as a vector backbone. We pose a generalized learning framework for Syntactically Aware Embeddings (SAEs) that extend across learning domains. We pretrain SAE models using a small amount of data (~ 30,000 samples) and test downstream performance on a variety of learning domains and tasks. We test SAEs on the NLP (GLUE and CoNLL benchmarks) and biochemical (SMP) domains. Across the NLP domain, the SAEs outperform baseline embeddings on 10/11 tasks. In the biochemical domain, we observe improvements in 11/20 tasks. SAEs show particular promise in settings with limited fine-tuning when compared to baselines.
Learning on the Job: How Teachers Learn to Adapt Their Approaches to Conversations About Racism
Keywords: Education
Race
Racism
Education
Learning
Teachers
Text
Thesis

In recent years, amplified by the unjust murders of George Floyd, Breonna Taylor, and countless other Black lives, there has been a national push to better educate our citizens about race and racism. In response, there have also been attempts to limit these discussions, with some states passing bills that prohibit teachers from talking about racism, white supremacy, and Critical Race Theory. However, in order to help our students develop into empathetic and informed citizens, discussions of race and racism must occur in classrooms. Due to the flexibility of their subject and the focus on identity development, California high school English teachers are uniquely situated to highlight these discussions in their classrooms, yet many teachers do not feel prepared or comfortable to do so early in their careers. In order to better understand how teachers’ level of comfort with and approaches to these conversations change throughout their career, I conducted semi structured interviews with four California Bay Area high school English teachers, all of whom work at the same school and have been teaching for over a decade. My findings revealed most of the teachers felt uncomfortable having these discussions with their students at the beginning of their career, but they all viewed English teachers as having some level of responsibility to facilitate these discussions and struggled to engage different student beliefs. They all gained comfort with these discussions throughout their career and were able to adapt their approaches to these conversations accordingly by taking into account how they frame and model these conversations, learning from their mistakes, and establishing a strong classroom culture. Importantly, through working with colleagues who were equally open-minded and committed to this work and learning from their students, especially their Students of Color, they were able to gain comfort with their discomfort in these conversations by acknowledging that they have a great extent to learn from others’ lived experiences and commitment to this work.
Learning priors for neural scene representations
Keywords: Engineering.
Electrical Engineering.

Neural 3D scene representations have recently emerged as a novel way to store information about 3D environments, their properties, and behavior, with learned features. Their key distinction from classical computer graphics representations is that the parameters of a representation can be obtained through optimization with the objective to be consistent with observations. As such, they have become a transformative tool for combining techniques from computer graphics and machine learning to represent 3D scenes. These representations have found use in applications ranging from robotics and remote sensing to cinematography and video editing. However, these representations are limited by the fact that they are only able to leverage captured information in a specific single scene. Specifically, only observations of a single individual scene can be used to create and improve the quality of the neural representation of this scene. This leads to undesirable properties, like slow creation of neural scene representations from observations or inability to generate realistic entirely new neural scene representations. In this thesis, Learning Priors for Neural Scene Representations, I propose novel methods for creating neural 3D scene representations which leverage information learned from data beyond a single individual scene. I look into using 3D, images, and pre-trained models as sources of information from which neural 3D scene representation priors can be learned. Moreover, I show how building standard computer graphics methodology into our neural scene representation architecture can ease the learning of priors from data which does not capture full 3D geometry.