VictorJuiz/thesis huggingface dataset:
Full-field analysis : a machine learning approach
Keywords: Energy Resources Engineering.

The numerical reservoir simulation model is a stable among the tools used to address the problem of describing the fluid flow behavior of producing reservoir from wells. The downside of using these models is the inherent cost of development and deployment, especially for small fields, and resources restrained projects. The recent advances in machine learning methods in well testing domain, ignited an interest to bring these capabilities to reservoir simulation and management. In this work, supervised and unsupervised machine learning techniques were applied to reservoir management tasks, that are usually dealt with using a numerical reservoir simulation model. Starting with well level analysis, simulation of downhole pressure response in producing wells, as a function of production and /or injection rate, using only field data is investigated. The methods used include algorithms composed of feed forward, recurrent, and convolution layers. The same is used to simulate water cut response as a function of operational parameters. The results suggest that it is possible to generate accurate prediction using these techniques. Extending the analysis to field level, we showed how using unsupervised learning techniques helped in guiding samples selection for training, then we applied generative modeling techniques using variational autoencoder to the problem of spatial control in the reservoir form data. We compared the performance of it to autoencoder, and other machine learning algorithms to predict multiphase production profiles form wells. Our investigation indicates that it can be done successfully in undrilled locations. We further applied conditional variational autoencoder along with deep feature interpolation methods to generate novel simulations that are not available in the training data set, extending the range of available simulated profiles without additional training or the need to conduct numerical simulation runs. Finally, we showed, using real field examples, how the methods developed can be used in predicting wells' production performance in the future, using past production data, and wellhead surface measurements, and control as an input. We applied this to single, two-, and three-phase flow examples successfully. We compared the liquid rate production performance to Gilbert correlation, a commonly used solution for this problem. We showed that using machine learning algorithms carries less uncertainty in cumulative production estimation, compared to Gilbert. The solutions carries implications for cost, and field data collections strategies. The methodologies we employed assumes no reservoir model and are purely data driven
Functional analysis of polarity protein dynamics in the Arabidopsis stomatal lineage
Keywords: Biology.

Cell polarity features the asymmetric distribution of molecules and structures. The establishment of cell polarity allows cells to spatially organize diverse cellular processes, including orienting cell division planes, assuring directional transport of nutrients and hormones, and determining cell morphology and fate. In both animals and plants, cell polarity is established and maintained by so-called "polarity proteins", whose subcellular localization is markedly asymmetric. Precision in the polar localization pattern of polarity proteins, in both space and time, is crucial for the development and functions of the hosting cells. In the past two decades, many polarity proteins have been identified; the cellular functions of most of these polarity proteins, however, remain to be determined. In the stomatal lineage of Arabidopsis thaliana, polarity proteins BASL (BREAKING OF ASYMMETRY IN THE STOMATAL LINEAGE) and BRXf (the protein family of BREVIS RADIX) interact and polarly localize to a fraction of the membrane before the onset of an asymmetric cell division (ACD). How BASL and BRXf achieve and maintain their striking polarity and how the dynamics of their polarity in different stages of ACD are connected to their functions are largely unknown. In this dissertation, I combine long-term live imaging, quantitative image analysis, genetics, and developmental biology approaches to explore different avenues of BASL/BRXf polarity and the connections of their polarity to their functions in regulating ACD, thereby providing a mechanistic view of cell polarity and ACD regulations in plants
Function-oriented synthesis : design, synthesis, and evaluation of novel protein kinase C modulators
Keywords: Chemistry.

Protein kinase C (PKC), a family of central signaling kinases, is of immense research interest due to its prominent roles in various devastating diseases for which there are no cures. The first part of my dissertation research is concerned with bryostatin 1, a scarce marine natural product and a potent PKC modulator. Even though bryostatin 1 is in the forefront of multiple clinical investigations (HIV/AIDS eradication, Alzheimer's disease, cancer immunotherapy etc.), it is not evolved nor optimized for human use, and is unlikely to succeed in this one-size-fits-all approach. Therefore, design, synthesis, and evaluation of synthetically accessible bryostatin analogs through function-oriented synthesis continues to be important. From the scalable synthesis of bryostatin 1, I participated in a collaborative effort to prepare and evaluate close-in derivatives of bryostatin 1, adjuvant leads for enhanced cancer immunotherapy. Using computer-guided, function-oriented synthesis, I also developed an accessible synthesis of the most simplified bryostatin analogs to date, in which one analog possesses strikingly similar potency to that of bryostatin 1, the parent compound. I also developed an asymmetric bifunctional allylation platform that is amenable to broad substrate scopes, further improving the scalable synthesis of bryostatin 1. This methodology successfully led to the synthesis of a novel des-A-ring bryostatin analog that possesses single digit nanomolar binding affinity to a conventional and a novel PKC isoform. Lastly, I am also working on tigilanol tiglate that shares very close structural similarity with phorbol esters. However, unlike phorbol esters, a class of potent tumor promoters, tigilanol tiglate can rapidly ablate tumor and at the same time induce remarkable wound healing activity via intratumoral injection. Tigilanol tiglate is currently being clinically evaluated (Phase II) with a broad range of tumors, especially accessible cutaneous, subcutaneous, and nodal tumors that are refractory to conventional therapy. Its activity is proposed to be the result of selective activation of conventional PKC isoforms. Our work is the first, and currently the only, demonstration of laboratory preparation of tigilanol tiglate and analogs, which would have a huge impact on their future supply
Fundamental and applied physics with optically levitated microspheres
Keywords: Physics.

In this collection of work, I will first guide the reader toward an understanding of optical tweezers, a technique used to trap small refractive particles, such as glass microspheres or living cells. In 2019, Arthur Ashkin was awarded part of the Nobel Prize in Physics for his development of this technique, and he made many of the initial strides in the field. With a general understanding of optical tweezers, I will discuss the specific setup and implementation constructed in our group at Stanford, especially those aspects that are particularly unique or novel to the apparatus. All of the work presented has been performed with optically trapped silica microspheres in vacuum, using a single-beam trapping technique with interferometric readout. I will then move to a discussion of a number of technical developments with metrological applications including: force field microscopy, direct measurements of a levitated microsphere's mass and radius, control of the microsphere's rotational degrees of freedom, and residual gas sensing. To conclude, I will explore some of the fundamental science goals of the project, including results from searches for fifth forces related to both dark energy and more general modifications to Newtonian gravity that might arise from new physics that manifest in the submillimeter regime
Fundamental limits and optimal uses of data in modern learning problems
Keywords: Electrical Engineering.

What is the best we can do with the amount of data at our disposal with a given learning task? Modern learning problemsâwith a modest amount of data or subject to data processing constraintsâfrequently raise the needs to understand the fundamental limits and make judicious use of the available small or imperfect data. Given a learning problem, the aim of this thesis is to exploit its key structure, as well as optimally trade between real-world resources, to achieve statistical efficiency. This thesis covers three examples spanning statistics, information theory, theoretical computer science, operations management, and economics. The first example is the estimation of entropy of continuous distributions, an important and classic problem in information theory and statistics. In this problem, we identify and overcome the bottleneck caused by small densities, and provide the first characterization of the minimax risk without the prevalent assumption that the densities are bounded away from zero. The second example is the unified estimation of properties of discrete distributions. On one hand, we show that two approaches, based on either local moment matching or profile maximum likelihood, could both be universally optimal. On the other hand, we determine the fundamental limits of all unified estimators, and identify a tight separation between ad-hoc and unified approaches emerged only in the high-accuracy regime. The third example concerns the optimal bidding in first-price auctions, which have very recently swept the online ad industry. We characterize the optimal performances and propose computationally efficient algorithms, under various assumptions on the characteristics of the other bidders' bids, of the bidder's private valuation, of the feedback structure of the auction, and of the reference policies with which our bidder competes. Experimentation on first-price auction datasets from Verizon Media demonstrates the promise of our schemes relative to existing bidding algorithms
Fusion for robot perception and control
Keywords: Mechanical Engineering.

Humans have long dreamed of robots that can perform a wide variety of tasks, such as cooking, cleaning, and exploring potentially dangerous environments. However, robotics adoption still struggles even in highly-structured environments. In factories, robots currently account for less than one third of the manufacturing workforce. Because many robots need to be hardcoded for every task, they often cannot deal with any errors in their models nor any changes to the environment. In academic research, recent works in machine learning are enabling robots to learn directly from data. Particularly in the areas of learning-based perception and control, we see advancements in deep learning for visual perception from raw images as well as deep reinforcement learning (RL) for learning complex skills from trial and error. However, these black-box techniques often require large amounts of data, have difficult-to-interpret results and processes, and fail catastrophically when dealing with out-of-distribution data. In order to create robotic systems that can flexibly operate in dynamic environments, we want robot perception and control algorithms that have three characteristics: sample efficiency, robustness, and generalizability. In this dissertation, I introduce the concept of ''fusion'' in robot perception and control algorithms to achieve these three characteristics. On the perception side, we fuse multiple sensor modalities and demonstrate generalization to new task instances and robustness to sensor failures. On the control side, we leverage fusion by combining known models with learned policies, making our policy learning substantially more sample efficient
Gauge-theoretic invariants in algebraic geometry
Keywords: Mathematics.

In this thesis, we examine certain questions relevant to the study of sheaf-theoretic invariants of complex projective surfaces and beyond. In the first part, the virtual localization technique is applied to the problem of showing blow-up formulae for virtual invariants on a smooth projective surface by using the moduli spaces of m-stable sheaves in the sense of Nakajima and Yoshioka, who already proved analogous results for equivariant moduli spaces of framed sheaves on P2. This yields a very general blow-up formalism that is applied to prove a blowup formula for virtual Donaldson invariants. In the second part, the theory of the Atiyah class on algebraic stacks is developed. Here, several technical constructions are given. Some of these may have been known to experts but no suitable reference was found. The results provide the fundamental building blocks for the construction of perfect obstruction theories. They are used throughout the first part and may prove useful beyond this setting
Geometric aspects of deep learning
Keywords: Physics.

Machine learning using deep neural networks -- deep learning -- has been extremely successful at learning solutions to a very broad suite of difficult problems across a wide range of domains spanning computer vision, game play, natural language processing and understanding, and even fundamental science. Despite this success, we still do not have a detailed, predictive understanding of how deep neural networks work, and what makes them so effective at learning and generalization. In this thesis we study the loss landscapes of deep neural networks using the lens of high-dimensional geometry. We approach the problem of understanding deep neural networks experimentally, similarly to the methods used in the natural sciences. We first discuss a phenomenological approach to modeling the large scale structure of deep neural network loss landscapes using high-dimensional geometry. Using this model, we then continue to investigate the diversity of functions neural networks learn and how it relates to the underlying geometric structure of the solution manifold. We focus on deep ensembles, robustness, and on approximate Bayesian techniques. Finally, we switch gears and investigate the role of nonlinearity in deep learning. We study deep neural networks within the Neural Tangent Kernel framework and empirically establish the role of nonlinearity for the training dynamics of finite-size networks. Using the concept of the nonlinear advantage, we empirically demonstrate the importance of nonlinearity in the very early phases of training, and its waning role farther into optimization
Geometric techniques in multiterminal communication and estimation
Keywords: Electrical Engineering.

Since its inception in 1948, one of the main goals in information theory has been to extend its original scope of point-to-point communication to include networks of nodes exchanging information. Towards this goal, we develop tools from high-dimensional geometry to analyze the fundamental limits of communication and statistical estimation tasks in a networked setting. In the first half of the thesis, we describe a problem in network communications -- the relay channel -- and prove a new upper bound on the capacity of this channel which resolves an open problem posed by Thomas Cover in "Open Problems in Communication and Computation", Springer-Verlag, 1987. The proof is highly geometric, with its main ingredient being a new isoperimetric result on high-dimensional spheres that builds on a Riesz-type rearrangement inequality. In the second half of the thesis, we consider a collection of networked statistical estimation problems modeling bandwidth and privacy constraints in distributed and federated learning systems. In these problems, data is distributed across many nodes in a network and must be communicated to a centralized estimator under communication, privacy, or mutual information constraints. We show how a geometric interpretation of Fisher information from the processed statistical samples can derive tight minimax lower bounds for many distributed estimation problems of interest
Go west, young Han : internal migration as a strategy of counterinsurgency
Keywords: Political Science.

Why was China able to insulate Xinjiang from Muslim insurgency for a generation during the Maoist period (1949-76) but not later (1981-2015)? The pattern challenges conventional understandings of "sons of the soil" conflicts because Muslim insurgency in Xinjiang decreased when Han in-migration to the region escalated, conversely increasing as Han in-migration declined. By considering counterinsurgency as a delegation problem, I show that state-supported internal migration through a multifunctional agency can be a strategy to reduce -- rather than cause indigenous insurgency. I analyze how the bingtuan in Maoist China served as a multifunctional agency for counterinsurgency -- an organization of Han migrants with the dual function of an armed force and farmers who supported their own livelihood through wasteland reclamation. The bingtuan's dual function provided a low-cost solution for precision-based counterinsurgency by removing the need for effective coordination between multiple agencies that each specialize in military force or intelligence collection. The threat of Soviet subversion via Xinjiang's Muslim population made the use a multifunctional agent optimal for targeting active insurgents precisely, without creating new grievances through indiscriminate repression. The Maoist state's command over key economic resources provided the requisite state capacity to induce compliance from its multifunctional agents. After Mao, declines in both foreign threat and a centrally controlled economy have led the state to use multiple counterinsurgent agencies that were unable to coordinate effectively, resulting in the re-emergence of Muslim insurgency. I test my theory through both within-Xinjiang analyses and cross-regional comparisons between Xinjiang and other peripheral provinces of China, Heilongjiang and Tibet. Evidence is based on my ethnographic fieldwork, interviews with professionals and officials, and an original collection of 550 hitherto untapped historical gazetteers
Going Green with the Green and Gray: How Interpretive Rangers' Perspectives Shape Climate Change Education in Yosemite National Park
Keywords: Graduate School of Education
interpretive park rangers
National Park Service
Yosemite National Park
climate change education
climate change interpretation
interviews
Thesis

Climate change is a clear and present danger to national parks and other protected areas across the United States where rising annual average temperatures and falling precipitation levels are resulting in a myriad of potentially devastating effects. These same parks are also tremendous sources of hope because they provide opportune settings for impactful climate change education through the interpretive programs they offer. To contribute to the development of such programs, this thesis aims to uncover how the experiences, identities, and perceptions of interpretive park rangers influence their approach to climate change interpretation. Through semi-structured interviews with six current and former Yosemite National Park (CA) interpretive rangers, I explored the rangers’ personal backgrounds, relationship to nature and Yosemite, climate change beliefs, and thoughts on climate change interpretation. I found that the rangers experience powerful internal conflicts related to when, whether, and how they include climate change in their interpretive programming. Those conflicts arise as their personal beliefs about climate change, relationship to Yosemite, and sense of professional responsibility to the park and visitors create tension with their (mis)conceptions (according to visitor studies) that climate change interpretation may spoil visitors’ experiences or instigate unwanted confrontation. This tension compromises rangers’ confidence in delivering climate change interpretation faithful to their own identities and deeply held beliefs. Equipped with a better understanding of park visitors’ beliefs and interests and with ongoing professional development, interpretive park rangers may be better supported institutionally and personally to leverage their identities, beliefs, and perspectives to maximize their efficacy as climate change educators.
The H3.3 G34W mutation disrupts the balance between H3K27 and H3K36 methylation to corrupt cellular identity in giant cell tumor of bone and an oncogenic role for ASH1L in lung adenocarcinoma and possibly multiple human cancers
Keywords: Biology.

The precise temporal and spatial coordination of histone lysine methylation dynamics across the epigenome regulates virtually all DNA-templated processes. A large number of histone lysine methyltransferase (KMT) enzymes catalyze the various lysine methylation events decorating the core histone proteins. Mutations, genetic translocations and altered gene expression involving these KMTs are frequently observed in cancer, developmental disorders and other pathologies. Therapeutic compounds targeting specific KMTs are currently being tested in the clinic, although overall drug discovery in the field is relatively under-developed. In chapter 1, we review the biochemical and biological activities of histone KMTs and their connections to human diseases, focusing on cancer. We also discuss the scientific and clinical challenges and opportunities in studying KMTs. In chapter 2, we characterize the role of the oncogenic H3.3 G34W mutation in Giant Cell tumor of the bone. To do so, we have created a novel cellular model of the disease using primary human mesenchymal stem cells. Using this model, along with biochemical assays, we determine the causal mechanistic consequences of the H3.3 G34W mutation and identify a novel potential mechanism for crosstalk between epigenetic modifications. In chapter 3, we discuss the role of the ASH1L in human cancers. We find that chromosomal amplification or gain of ASH1L is a highly recurrent event in many tumor types, including Lung Adenocarcinoma (LUAD). We interrogate the specific mechanisms involved in tumorigenesis of Lung Adenocarcinoma (LUAD) using biochemical methods as well as human LUAD cell lines to determine the mechanisms by which ASH1L contributes to tumorigenesis. We also utilize murine models of LUAD to determine the in vivo contributions of ASH1L to the process of tumorigenesis, and find that ASH1L plays a critical role in genetic models of LUAD. In chapter 4, we present a broader view of the field of cancer epigenetics in the context of H3K27 and H3K36 methylation. We highlight several outstanding questions of importance in the field, and nascent opportunities to answer them. We also discuss clinical opportunities on the horizon and the relationship they might have with these outstanding questions