VictorJuiz/thesis huggingface dataset:
Cybersecurity Magic: Parallel Structures of Design by Hackers and Magicians
Keywords: magic
cybersecurity
hacking
white hat hackers
magician
technology
deception
sleight of hand
Thesis

Almost half of the American public has been a victim of a major cyber-hacking incident and cyber-crime now tops the Gallup Poll’s list as the crime Americans worry about the most. (Pagliery 2014; Riffkin 2017) Yet for many Americans, their vulnerability to hacking seems mysterious. How can so much cyber-crime occur when so many technical efforts are devoted to preventing it? One possible answer to this question might be that cyber-hackers take advantage of common human psychological traits that make us all vulnerable to deception and misdirection. The ability to take advantage of such human vulnerabilities is part of the art of deception in many realms, such as espionage, warfare, politics, theatre, and especially in performance magic. What are the similarities and differences between the process used by cyber-criminals to design their hacking attacks and the process used by magicians to design their magic tricks? I investigated these connections by collecting primary sources and interviewing a small number of magicians, “white hat hackers,” and individuals from the national security intelligence community to discover how they design their activities to take advantage of individuals’ common human vulnerabilities and new technology vulnerabilities. I present multiple case studies and then compare specific magic tricks with specific cybersecurity exploits, demonstrating how the design processes used by hackers have inherent structural similarities to those used by magicians. Furthermore, this research demonstrates the value in demystifying the design processes of hackers and magicians to produce better cybersecurity.
Data-driven transcriptomic modules
Keywords: Bioengineering.

High throughput measurement of gene expression levels have dramatically increased the amount of biological information we collect from experiments, and presents a tantalizing possibility of gaining novel insights into human biology at the molecular level. Realization of this possibility is dependent on our ability to interpret the tens of thousands of numerical values, and to draw inferences from the patterns. Analysis of these data typically begins with the identification of genes that are differentially expressed between two or more groups. Unfortunately, this either entails testing all the genes (and hence suffer from low statistical power), or using pre-defined subsets that introduce researcher bias. In recent years, it is more common for researchers to perform Gene Set Enrichment Analysis (GSEA), which restores statistical power by testing at the gene set level. The choice of which gene sets and how many of them to test is, however, a source of researcher bias. Furthermore, the reliance on pre-defined gene sets means that we are, once again, limited to our current understanding of human biology. Other problems that have plagued transcriptomic analysis include inter-platform incompatibility and batch effects. This dissertation seeks to provide an alternative approach for transcriptomic research. Specifically, I describe a data-driven methodology for obtaining transcriptomic modules from a large compendium of microarray experiments, and demonstrate how these modules can be used to perform classification of samples and also to extract biologically-meaningful properties from gene expression datasets. I also describe an imputation method to allow cross-platform analysis between two of the most common microarray platforms in the Gene Expression Omnibus, and use this to transform the drug-gene database, Connectivity Map (CMap), into a compatible form for the transcriptomic module analysis. Finally, I demonstrate how drug repurposing could be achieved using CMap in the framework of these transcriptomic modules.
Data-Space Inversion with Variable Well Controls in the Prediction Period
Keywords: School of Earth Energy &amp; Environmental Sciences
Data-space inversion
Variable controls
Optimization
Thesis

Data-space inversion (DSI) and related procedures represent a family of methods that provide posterior (history-matched) predictions for quantities of interest, along with uncertainty quantification, without constructing posterior models. Rather, posterior predictions are generated directly from a large set of prior-model simulations and observed data. Current DSI methods require that well controls (e.g., bottom-hole pressures) be specified for the full duration of the simulation time frame (historical period plus prediction period). This means that, if well settings are changed, the full set of prior models must be re-simulated using the new controls, which would be very time consuming for optimization computations.
In this work we develop a data-space inversion with variable controls (DSIVC) procedure. DSIVC enables the generation of posterior forecasts, under user-specified well controls in the post-history-match period, without re-running any prior simulations. In DSIVC, we first perform flow simulations on all prior realizations with randomly generated well controls (with a prescribed number of stages). User-specified controls are then treated as additional `observations' to be matched in posterior predictions. Posterior data samples are generated using a randomized maximum likelihood procedure in data space. Data-space principal component analysis and histogram transformation are applied to improve DSIVC performance and efficiency.
Extensive results are presented for a bimodal channelized system. Different (synthetic) true models, and different sets of post-history-match controls, are considered. For any well control specification, a set of 100 posterior predictions can be generated in seconds or minutes. Posterior predictions from our DSIVC procedure are compared to those from an existing DSI method for a number of cases. The DSI results require all prior models to be re-simulated using the specified controls, while DSIVC requires only one set of prior simulation runs. Substantial uncertainty reduction is typically achieved through data-space inversion, and general agreement between DSIVC and DSI results is consistently observed. The DSIVC method is then applied for well control optimization under uncertainty. A mesh adaptive direct search algorithm is used to maximize expected net present value (NPV) over 100 posterior predictions. Clear improvement in expected NPV is achieved using DSIVC. The method may thus be suitable for use in closed-loop reservoir management, with both data assimilation and production optimization performed in data space.
Deciphering the regulation of A-to-I RNA editing in the Drosophila brain [electronic resource]
Keywords: Genetics.

RNA editing increases genetic diversity by altering RNA sequences from those encoded by the genome. The most prevalent type of RNA editing is adenosine-to-inosine (A-to-I) RNA editing, where the Adenosine Deaminase Acting on RNA (ADAR) protein family converts adenosines into inosines, which are then recognized as guanosines by the cellular machinery. ADAR enzymes are conserved in numerous species including Drosophila, where dADAR edits over 5,000 sites at widely varying frequency, creating different ratios of modified transcripts. These editing events are necessary for proper neuronal function of the fly; however, the regulation of editing levels at particular editing sites is poorly understood. This thesis addresses the two primary modes of regulation of RNA editing in Drosophila, cis and trans regulation. Chapter 1 provides an introduction to ADAR editing, the regulatory role of both the cis determinants and the trans factors that influence editing levels, and the function of RNA editing in the brain. In Chapter 2, I evaluate RNA editing level differences between different neuronal cell types in the fly brain, uncovering the extent to which RNA editing is differentially regulated in different cell types. Next, in Chapter 3, I analyze how both cis and trans regulatory factors contribute to RNA editing differences between two closely related fly species, D. melanogaster and D. sechellia, highlighting the critical role of cis sequence changes in altering RNA structural stability around editing sites. Finally, in Chapter 4, I discuss the novel and evolutionarily conserved role of the RNA binding protein Zinc-finger protein at 72D (Zn72D) as an ADAR-interacting protein and regulator of RNA editing. Together, these chapters provide insight into the variability of editing levels within the brain and the cis and trans regulatory factors that influence these editing events, improving our understanding of this critical co-transcriptional process.
Deep episodic value iteration [electronic resource] : a theory of sample efficient learning for machine learning and cognitive (neuro) science
Keywords: Psychology.

This thesis aims to show that, with some extensions, deep learning is computationally capable of addressing rapid adaptation, can account for behavioral phenomena surrounding this ability, and is parsimonious with existing theories regarding the functional role of the relevant neuroanatomy. The main contribution of this thesis is an algorithm that can account for planning from limited data, which we call Deep Episodic Value Iteration (DEVI). DEVI uses a deep neural network to learn a similarity metric, which is applied recursively for planning over episodic memories. By training this network end-to-end based on planning performance, we posit that DEVI should be capable of meta reinforcement learning, even in high dimensional state spaces. We evaluate DEVI in comparison with traditional deep learning techniques as well as other approaches to meta learning. We close by showing how DEVI can be seen as a computational account of schema formation and schema-consistent learning.
Deep learning in-vivo transcription factor binding
Keywords: Biophysics.

Transcription factors (TFs) affect gene expression by interpreting regulatory DNA within a genome. Models of DNA sequence and shape can explain in-vitro TF-DNA interactions outside a cellular context. But in-vivo TF-DNA interactions in cells are influenced by additional factors, such as interactions between TFs, and interactions between TFs and nucleosomes. Here, we present the application of deep learning, a class of modern machine learning techniques, to the task of modeling in-vivo transcription factor binding at a genome-wide scale. Deep learning has powered significant breakthroughs in complex pattern recognition tasks across several data-rich domain and successful applications have primarily focused on image, speech, and text data modalities. In this thesis, we present three new contribution to the field of deep learning applications to genomics: (1) Adapation of deep learning methods to regulatory DNA sequence data using simulations, (2) development of deep learning models of in-vivo TF binding at a genome-wide scale, and (3) interrogations of these models to reveal determinants of in-vivo TF binding sites. Our results demonstrate that deep learning can be used to build accurate, interpretable models of in-vivo TF binding. That there are guiding principles to systematic development and interpretation of such models. Finally, we discuss limitations of our methods and point to directions for future work.
Deep learning methods for modeling interrelations of dynamic and static objects
Keywords: Electrical Engineering.

We are witnessing an increasing growth of autonomous platforms such as self-driving cars and social robots around us. These smart machines will be navigating and interacting with us in various scenarios. However, understanding the behavior and relations between the humans and objects in different scenes is very important for these smart agents navigating in this environments. Modeling the interrelations among objects and humans enables the computer vision systems to better understand complex scenes and it comes with many challenges: 1) How to model the complex scene context? 2) How to model the interrelations among objects and humans? 3) How to capture and model the unwritten rules of human social behavior? The goal of this thesis is to address some of these challenges. Modeling the inter-relations of dynamic and static objects or agents within an environment applies to a wide range of domains from autonomous driving vehicles and social robot navigation, to abnormal behavior detection in surveillance. For example, for an autonomous vehicle to navigate in a space, it has to understand the surrounding environment and model the inter-relations of dynamic and static objects, i.e. the relationship between the agent and the static objects, the moving objects and their joint interactions. To this end, we need to track all moving objects (known as multi-object tracking problem), predict their future positions and interactions with the environment (known as path prediction problem), and find the traversability of a path given the state of static and moving objects (knows as traversability estimation). To tackle these problems we proposed several new deep learning methods as follows: 1- In the first chapter of this thesis, we tackle the multi-object tracking problem, we proposed an online method that encodes long-term temporal dependencies across multiple cues using deep learning. Our framework uses a structure of Recurrent Neural Networks (RNN) that jointly reasons on multiple cues over a temporal window. 2- The second part of this thesis (Chapters 2 and 3) tackles the problem of path prediction, we proposed two interpretable frameworks CAR-Net and Sophie based on Attentive Generative Adversarial Network (GAN), that can leverage two sources of information: the past trajectory of all the agents in the scene and a wide top-view image of the navigation scene. Our predicted trajectories are obeying physical constraints of the environment while anticipating the movements and social behavior of other people. 3- Finally, in the last part of the this thesis, chapters 3 and 4 tackle the traversability estimation problem, we proposed GONet and GONet++ which uses several deep learning networks trained on a large amount of data from a single RGB camera to identify the traversable and non-traversable spaces in an environment around an agent. All proposed approaches were evaluated on relevant benchmarks and outperform all previous state-of-the-art methods.
Deep learning on point clouds for 3D scene understanding
Keywords: Electrical Engineering.

Point cloud is a commonly used geometric data type with many applications in computer vision, computer graphics and robotics. The availability of inexpensive 3D sensors has made point cloud data widely available and the current interest in self-driving vehicles has highlighted the importance of reliable and efficient point cloud processing. Due to its irregular format, however, current convolutional deep learning methods cannot be directly used with point clouds. Most researchers transform such data to regular 3D voxel grids or collections of images, which renders data unnecessarily voluminous and causes quantization and other issues. In this thesis, we present novel types of neural networks (PointNet and PointNet++) that directly consume point clouds, in ways that respect the permutation invariance of points in the input. Our network provides a unified architecture for applications ranging from object classification and part segmentation to semantic scene parsing, while being efficient and robust against various input perturbations and data corruption. We provide a theoretical analysis of our approach, showing that our network can approximate any set function that is continuous, and explain its robustness. In PointNet++, we further exploit local contexts in point clouds, investigate the challenge of non-uniform sampling density in common 3D scans, and design new layers that learn to adapt to varying sampling densities. The proposed architectures have opened doors to new 3D-centric approaches to scene understanding. We show how we can adapt and apply PointNets to two important perception problems in robotics: 3D object detection and 3D scene flow estimation. In 3D object detection, we propose a new frustum-based detection framework that achieves 3D instance segmentation and 3D amodal box estimation in point clouds. Our model, called Frustum PointNets, benefits from accurate geometry provided by 3D points and is able to canonicalize the learning problem by applying both non-parametric and data-driven geometric transformations on the inputs. Evaluated on large-scale indoor and outdoor datasets, our real-time detector significantly advances state of the art. In scene flow estimation, we propose a new deep network called FlowNet3D that learns to recover 3D motion flow from two frames of point clouds. Compared with previous work that focuses on 2D representations and optimizes for optical flow, our model directly optimizes 3D scene flow and shows great advantages in evaluations on real LiDAR scans. As point clouds are prevalent, our architectures are not restricted to the above two applications or even 3D scene understanding. This thesis concludes with a discussion on other potential application domains and directions for future research.
Defining a Role for RSK in Normal and Aberrant Myelopoiesis
Keywords:  Department of Biology
Biology
RSK
CREB
myelopoiesis
hematopoiesis
acute myeloid leukemia
Thesis

Recent studies have established that the Ribosomal S6 Kinases (RSKs) drive cell growth, survival, and proliferation in solid cancers including breast, lung, and prostate. However, the role of RSK in normal and malignant blood cell formation, or hematopoiesis, remains undetermined. RSK phosphorylation targets such as the transcription factor cyclic AMP response element binding protein (CREB) are known to be regulators of hematopoietic differentiation as well as potential therapeutic targets for acute myeloid leukemia (AML). Here, we describe the function of RSKs in hematopoietic differentiation and growth. RSK1 and RSK2 were identified as the predominant isoforms in hematopoietic cells, particularly in the myeloid lineage. RSK inhibition using the small molecule inhibitor BI-D1870 selectively reduced AML cell survival and development as compared to normal hematopoietic cells. When utilized to investigate the isoform-specific function of RSKs in hematopoiesis, small hairpin RNA (shRNA) knockdown of either RSK1 or RSK2 in AML cells failed to induce a growth defect. Additionally, knockdown of RSK1 and RSK2 together slightly weakened CREB activation but also did not impair cell growth, indicating that residual expression and redundant function of RSKs in knockdown cells allowed for relatively normal growth of RSK knockdown AML cells. A large reduction in AML cell growth was only observed when RSK1 or RSK2 were completely knocked out using CRISPR. Taken together, our data demonstrate that RSK is crucial for the tumorigenesis and proliferation of AML cells as well as normal myeloid development (myelopoiesis). Increased susceptibility of AML cells to RSK inhibition signify RSK’s potential as a future therapeutic target for AML.
Defining the trophic role of a ubiquitous kelp forest echinoderm, the bat star Patiria miniata
Keywords:  Department of Biology
Stanford Department of Biology
Marine Ecology
Trophic Ecology
Food Web
Patiria miniata
Kelp Forest
Sea Star
Wasting Disease
Stable Isotopes
Niche Overlap
Thesis

Trophic interactions play crucial roles in structuring complex ecosystems. The bat star (Patiria miniata) is an abundant kelp forest echinoderm whose trophic ecology remains poorly understood. Though it has traditionally been considered an omnivorous generalist, most work on Patiria has focused on specific effects of predation on select groups of flora and fauna, with little consideration of its overall role in the kelp forest ecosystem. Understanding Patiria’s trophic role is especially important given the recent declines in many asteroid species due to sea star wasting disease. Patiria was left mostly untouched by the outbreak and could potentially fill the niches left by other ecologically-important sea star species. We used a combination of feeding experiments and comparative stable isotope analysis to examine various aspects of Patiria’s trophic ecology. Contrary to Patiria’s omnivorous reputation, initial feeding experiments revealed a predominantly carnivorous diet with little evidence of herbivory. Perplexed by these results, we conducted additional experiments which suggested that Patiria may feed on the epiphytic communities growing on algae, but not on the algae themselves. Finally, isotopic comparisons of bat stars with giant sea stars (Pisaster giganteus), purple sea urchins (Strongylocentrotus purpuratus), and warty sea cucumbers (Parastichopus parvimensis) revealed that bat stars occupy a high trophic level, have relatively low trophic diversity, and are isotopically most similar to giant sea stars (a carnivorous species). Altogether, these results suggest that Patiria has a narrower niche than previously expected and a trophic role similar to that of carnivorous sea stars like Pisaster.
The design, synthesis and evaluation of guanidinium-rich molecular transporters for the delivery of antibiotics and oligonucleotides
Keywords: Chemistry.

The delivery of cell-impenetrable therapeutic agents across biological barriers represents a challenging problem in the field of life science research and medicine. Many bacterial pathogens are inherently resistant to glycopeptide antibiotics such as vancomycin due to the antibiotic's relatively large size and mode of action, which requires actively growing cells. Recently, genetically-resistant vancomycin strains have emerged, adding to vancomycin's inefficaciousness against certain bacterial populations. The emergence and understanding of phenotypically and genetically resistant bacteria coupled to a declining pipeline of antibiotic development necessitates new strategies to combat bacterial infections. As part of a collaboration between the Wender and Cegelski labs, we have designed, synthesized and evaluated a novel vancomycin cell-penetrating peptide conjugate. We have shown that this new conjugate exhibits a unique mode of action compared to vancomycin, allowing it to be extremely effective against vancomycin-insensitive bacterial populations.
Destandardizing the standard (s) : ideological and implementational spaces in preservice teacher education
Keywords: Education.

New teachers and teacher education programs in the US are expected to be culturally and linguistically responsive to the ever-diversifying demographic landscape of public schools. However, professional and content standards still rely on categories and language conceptions that are rigid, static or, at best, ambiguous, such as "English Language Learners" or "Standard English." This interpretive dissertation engages with this intellectual and practical tension from a dual perspective, Language Anthropology and Language Policy. First, a California policy document (the Teaching Performance Expectations, TPEs) is analyzed to identify language conceptions and their process of textual construction. Then, successive empirical chapters identify how student teachers and teacher preparation instructors endorse and counter linguistic ideologies in order to reappropriate and contextualize the target policy. Ultimately, it is argued that educators can use their agency to construct alternative ideological spaces that encompass dynamic and fluid notions of language. Such "heteroglossic ideological and implementational spaces" (Flores and Schissel, 2013) are emancipatory for both educators and students and sustain the language practices of minoritized populations.