VictorJuiz/thesis huggingface dataset:
Efficient Erasure Coding in Distributed Storage Systems
Keywords: distributed storage system
erasure coding
HDFS
local reconstruction code
MapReduce
regenerating codes

Distributed storage systems store a substantial amount of data on many commodity servers. As servers failures are common, it is critical for distributed storage systems to store redundancy to tolerate such failures. Conventionally, a distributed storage system replicates data as the redundancy. Recently, erasure coding has been increasingly replacing replication thanks to its lower storage overhead. However, in many scenarios, erasure coding incurs additional overhead, such as higher network traffic, or lowers the performance of data accesses. In this dissertation, we address some of such challenges in two broad areas. Erasure coding with the optimal network overhead. Traditional erasure codes incur high network overhead when data needs to be reconstructed after a server failure. We study the problem of constructing erasure codes that consume the optimal network traffic to reconstruct data from multiple failures. We start from a new construction of minimum-storage cooperative regenerating (MSCR) codes that reconstruct data from two failures with the optimal network traffic. We show that an existing minimum-storage regenerating (MSR) code is also an MSCR code for two failures, and vice versa. For more general cases, we propose Beehive codes that optimize the volume of network traffic to reconstruct data from more than two failures, with storage overhead only slightly higher than optimum. I/O efficient erasure coding and systems. Traditionally erasure coding incurs higher I/O overhead because of its encoding and decoding operations. In this dissertation, we propose solutions to minimize the overhead of writing and reading erasure-coded data. On the input side, we design and implement Mist, a new mechanism for disseminating erasure-coded data efficiently to multiple receiving servers in data centers. On the output side, we exploit the demand skewness in distributed storage systems and propose Zebra, a framework that encodes data into multiple tiers dynamically by their demand to reduce the overall overhead to read erasure-coded data. We also investigate the data parallelism of erasure coding, which may affect the performance of running parallel data processing jobs on the erasure-coded data, such as MapReduce, and construct Carousel codes that allow data parallelism to be expanded into an arbitrary number.
Analysis of Ecological Communities Using Mixture Models
Keywords: microbiome
mixture models
sparsity
species distributions
statistical ecology
total species

In this dissertation we develop methodology to analyse the structure of ecological communities. Distributions of species within and between communities are characterized by highly skewed and heavy tailed distributions which often causes sparsity in an observed sample. We develop methodologies to account for these factors, focusing on three problems of interest. The first is estimating total species in a community. This quantity is an important measure of community health and is often used to assess sample quality, but due to sparsity when sampling rare species it is not readily determined. Two approaches are proposed, the first models the underlying rate distribution with a mixture distribution and estimates the expected number of zeros when mixed with a Poisson. The mixture distribution is estimated by minimizing a least squares objective function of observed and expected counts or by sampling a Multinomial-Dirichlet mixture. The second approach estimates total species by modelling the detection rate of new species with a parametric function. Methods of model selection and estimates of variance are presented for both approaches. For the second problem, we look at modelling the distribution of a species across multiple sites. Firstly, we differentiate structural zeros from sparse sampling zeros, an important distinction when modelling the underlying rates or fitting a presence-absence model. We also model the outcome as a mixture distribution to stratify the data, as observations from the low rates and heavy tail cannot be analysed on the same scale. The model is estimated using either the least squares or Multinomial-Dirichlet approach, adjusted for varying numbers of observations between sites. A modification of the expectation-maximization algorithm is also proposed to optimize the fit. For the third part, we focus on identifying factors that significantly affect community composition. We use the mixture distribution approach to estimate site specific distributions, then compute pairwise distances between sites to obtain the test statistic for a permutation test. Each method is tested via simulation, compared against current methods and illustrated with an applied example. The methods are motivated by analysis of microbiome data, but are designed to be generally applicable.
Enacting a pedagogy of multiliteracies: A study of six literacy teacher educators
Keywords: Literacy
Literacy teacher educators
Multiliteracies
Teacher education

This study examined the knowledge, dispositions, beliefs, experiences, and educational practices of six literacy teacher educators, from three countries, who enacted a multiliteracies approach to teaching. In this qualitative study three semi-structured interviews were conducted of each participant over a three year period. Four significant findings emerged. First, these literacy teacher educators held a broad, holistic, and evolving conceptualization of literacy that was not limited to a notion of literacy as a set of autonomous skills (e.g., reading and writing). This conceptualization included a range of multimodal communications: print-based literacies, arts-based experiences, and digital practices. Second, all of the literacy teacher educators designed coherent courses that were driven by a single overarching purpose (e.g., developing a broad and inclusive understanding of literacy and literacy practices). The overarching purpose was realized through17 specific goals and purposeful assignments. Third, all of the literacy teacher educators were challenged by government restrictions, particularly the participants from England and Australia. The final finding revealed these literacy teacher educators held a unique set of dispositional qualities: a positive and progressive attitude, persistence, and passion; and their attitudes, skills, and knowledge were developed over time and with assistance. Implications for literacy teacher educators include the need for incorporating multimodal forms of expression and communication; addressing issues of social justice; remaining connected to the local community and the practicum aspect of the program; and, creating coherent courses. Implications for preservice teacher education programs include universities creating opportunities for professional development for teacher educators particularly in multiliteracies practices and providing mentors; developing hiring practices that include a multiliteracies interest; and, developing support against deleterious teacher education reforms.
Greedy Dictionary Learning Algorithms for Sparse Surrogate Modelling
Keywords: Dictionary learning
Greedy algorithm
Machine learning
Regression
Supervised learning
Surrogate modelling

In the field of engineering design, numerical simulations are commonly used to forecast system performance before physical prototypes are built and tested. However the fidelity of predictive models has outpaced advances in computer hardware and numerical methods, making it impractical to directly apply numerical optimization algorithms to the design of complex engineering systems modelled with high fidelity. A promising approach for dealing with this computational challenge is the use of surrogate models, which serve as approximations of the high-fidelity computational models and can be evaluated very cheaply. This makes surrogates extremely valuable in design optimization and a wider class of problems: inverse parameter estimation, machine learning, uncertainty quantification, and visualization. This thesis is concerned with the development of greedy dictionary learning algorithms for efficiently constructing sparse surrogate models using a set of scattered observational data. The central idea is to define a dictionary of basis functions either a priori or a posteriori in light of the dataset and select a subset of the basis functions from the dictionary using a greedy search criterion. In this thesis, we first develop a novel algorithm for sparse learning from parameterized dictionaries in the context of greedy radial basis function learning (GRBF). Next, we develop a novel algorithm for general dictionary learning (GGDL). This algorithm is presented in the context of multiple kernel learning with heterogenous dictionaries. In addition, we present a novel strategy, based on cross-validation, for parallelizing greedy dictionary learning and a randomized sampling strategy to significantly reduce approximation costs associated with large dictionaries. We also employ our GGDL algorithm in the context of uncertainty quantification to construct sparse polynomial chaos expansions. Finally, we demonstrate how our algorithms may be adapted to approximate gradient-enhanced datasets. Numerical studies are presented for a variety of test functions, machine learning datasets, and engineering case studies over a wide range of dataset size and dimensionality. Compared to state-of-the-art approximation techniques such as classical radial basis function approximations, Gaussian process models, and support vector machines, our algorithms build surrogates which are significantly more sparse, of comparable or improved accuracy, and often offer reduced computational and memory costs.
Influenza A Virus NS1 Inhibits Type I IFN Responses
Keywords: Influenza A virus
Innate immunity
NS1
Pathogen-host interactions
Type I interferon
Virus infection

Type I IFNs shape innate and adaptive immune responses by upregulating the expression of antiviral and immunomodulatory genes. Pathogenic viruses have evolved effective mechanisms to inhibit the type I IFN response, and IAVs are no exception – IAVs express NS1, a multifunctional protein that downregulates type I IFN production and signaling. The studies described herein characterize the contributions of both strictly conserved and strain-dependent residues in the NS1 putative SH2-binding domain (Y84) and the CPSF4-binding domain (F103 and M106), respectively, to NS1-mediated inhibition of type I IFN signaling and ISG expression. Using site-directed mutagenesis and reverse genetics, we generated rIAVs expressing NS1s encoding Y84F or F103L and/or M106I mutations. We provide evidence that a Y84F mutation abrogates NS1-mediated inhibition of IFN-β-inducible STAT1/2 phosphorylation and IFNAR1 expression. As a result, in human A549 lung epithelial cells and in C57BL/6 mice, rIAVs expressing NS1-Y84F replicate to lower titers and are more susceptible to the antiviral effects of IFN- treatment. rIAVs expressing NS1-F103L/M106I/F103L+M106I are also attenuated in A549 cells. Notably, in A549 cells infected with rIAV expressing NS1-M106I/F103L+M106I and treated with IFN- we identified increased polysome-associated ISG expression – indicative of increased translation initiation – compared with cells infected with rIAV expressing wt NS1. Viewed altogether, our data highlight the ability of IAV NS1 to antagonize multiple aspects of an effective host type I IFN response, and the importance of both conserved and strain-dependent residues in NS1 for IAV replication. In a supplementary chapter, we describe the elevated antiviral ISG signature present in the peripheral blood of three patients with Aicardi-Goutières Syndrome (AGS), an early-onset autoimmune disease associated with overactive type I IFN production. Our data suggest that ISG expression is a more sensitive biomarker for AGS than standard readouts for inflammation, including ESR, CRP, and WBC counts.
Fluorophosphonium Chemistry: Applying Strategies Learned from Boron to Phosphorus
Keywords: Electrophilic
Lewis Acid
Phosphonium

Since the inception of frustrated Lewis pair chemistry, interest in main group catalysts has undergone a resurgence. Central to the success of many main group systems is the pentafluorophenyl substituent, which provides both chemical stability and electrophilicity to the catalyst. Pentafluorophenyl substituents have been used with boranes, alanes and recently with fluorophosphonium cations. This thesis investigates a range of related aryl substituents applied to fluorophosphonium cations. Nitrene insertion into the bonds of borane substituents, including pentafluorophenyl groups, was used a means to tune the electrophilicity of main group systems. Sterically demanding perchlorophenyl substituents were employed to add protection to sensitive fluorophosphonium catalysts. The perchlorophenyl substituents also provided insight into the effect of mesomeric stabilization on electrophilicity of tetrahedral phosphorus systems. Perfluorobiphenyl and 3,5-bis(trifluoromethyl)phenyl substituents are also investigated as means of developing catalysts with electrophilicity exceeding that of pentafluorophenyl substituted phosphonium salts. Finally, binaphthyl substituents were applied to fluorophosphonium cations to generate chiral catalysts. The effect of constrained geometry was investigated using bridged binaphthyl substituted phosphorus species were investigated.
Three Essays in the Economics of Education
Keywords: Class Size
Dynamic Complementarities
Human Capital
Regression Discontinuity
School Accountability
School Finance

In principal, public education provides a child's key means of skill accumulation, irrespective of background. In practice, however, the actual performance of public schooling is a disappointment, with stakeholders concerned that the current state of public education heightens inequality and prepares students inadequately for the workforce or higher learning. This thesis develops and applies novel econometric techniques to highlight education policies that may increase student achievement and reduce the pervasive test score gaps that plague public education today. Chapter 1 sets out a new approach that enables me to credibly identify dynamic interactions among school inputs for the first time. Such an approach is rarely adopted in empirical research due to stringent requirements: in an observational setting, identifying dynamic interactions requires period-by-period randomization. I overcome this challenge by combining rich administrative data with a rule whereby students are held accountable only if there are forty or more students in their demographic group. The rule provides useful year-to-year variation, supplying the backbone of my identification strategy. I then estimate the technology structurally and consider the efficacy of alternative accountability schemes: conditioning on initial test scores rather than prior test scores can increase average achievement and reduce inequality. Chapter 2 proposes an approach that allows researchers to identify separate treatment components from a single discontinuity. As an application, I consider the discontinuity associated with class size caps -- a widespread education policy used to reduce class sizes. My approach exploits the asymmetry between school-grades entering versus exiting treatment to distinguish the pure effect of changes in class size from the effect of a newly-hired teacher. Using data from New York City, I find that class size reductions increase student achievement, though these gains are counteracted by the newly-hired teacher. Chapter 3 uses a regression discontinuity design to investigate the merits of decentralized public goods provision in the context of Title I, the largest U.S. federal education funding program. My results indicate that the negligible impact of Title I is caused by its centralized nature: in a decentralized form, Title I generates a substantial improvement in student achievement, particularly for the socioeconomically disadvantaged.
Synthesis of Boron, Silicon, and Phosphorus Lewis Acids and Frustrated Lewis Pair Complexes for C=O and C-F Bond Activations
Keywords: Catalysis
Inorganic synthesis

The recent proliferation of main group catalysts and stoichiometric reagents useful in effecting transformations of organic molecules has been augmented by development of the chemistry of frustrated Lewis pairs (FLPs). In seeking to both complement and emulate the reactivity of transition metal catalysts, both the synthesis of new Lewis acids and FLPs and the discovery of new reactivity with organic substrates has been explored. Boron Lewis acids such as B(C6F5)3 and electrophilic borenium cations are among the most commonly employed main group catalysts, particularly for the hydrogenation of unsaturated bonds. The first chapter of this thesis details explorations in modulating the Lewis acidity of neutral and cationic boron electrophiles by tuning phosphinimine and phosphine-amine ligands. A family of compounds featuring a P-N-B motif are presented. In seeking to broaden the range of main group catalysts, phosphorus Lewis acids have been explored. The third and fourth chapters presented herein detail the synthesis and catalytic activity of electrophilic phosphonium cations (EPCs). Synthesis of a chloride-substituted dication, and ferrocenyl-based mono- and dications are described, with a particular emphasis on their catalytic activity in the deoxygenation of ketones. The fifth chapter details the synthesis of phosphorus cations which attempt to model intermediates in EPC-mediated catalysis. Silylium cations are strongly fluorophilic and have previously been shown to cleave aliphatic C-F bonds. The final chapter presents examples of silylium cations in combination with weak phosphine Lewis bases for the selective cleavage of one C-F bond of an aryl-CF3 functionality. The FLP-captured PhCF2 is then converted to PhCF2H, completing a formal hydrodefluorination process of PhCF3 to PhCF2H. Analogous reactivity is observed with aryl-CF2 compounds.
Techniques for Constructing Efficient Lock-free Data Structures
Keywords: Balanced search tree
Concurrency
Data structure
Lock-free
Lock-freedom
Multithreading

Building a library of concurrent data structures is an essential way to simplify the difficult task of developing concurrent software. Lock-free data structures, in which processes can help one another to complete operations, offer the following progress guarantee: If processes take infinitely many steps, then infinitely many operations are performed. Handcrafted lock-free data structures can be very efficient, but are notoriously difficult to implement. We introduce numerous tools that support the development of efficient lock-free data structures, and especially trees. We address the difficulty of using single-word compare-and-swap (CAS) to develop lock-free graph-based data structures by introducing multiword synchronization primitives called LLX and SCX. These primitives fall between multi-compare-single-swap and full multiword-CAS in expressiveness, and can be implemented much more efficiently than multiword-CAS. We use them to implement a tree update template that can be followed to produce lock-free implementations of arbitrary updates in down-trees, and use this template to produce the first lock-free implementations of several advanced trees: chromatic search trees, relaxed AVL trees, and relaxed (a,b)-trees. We also introduce a new variant of a B-tree, called a relaxed B-slack tree, which has significantly better worst-case space complexity, and produce a lock-free implementation using our template. In these implementations, operations dynamically allocate memory for nodes. Additionally, in our implementation of LLX and SCX, each SCX operation allocates a descriptor, which contains information that another process can use to help the SCX operation complete. These implementations must perform dynamic lock-free memory reclamation, but traditional lock-free memory reclamation algorithms are either inefficient or cannot be used with our implementations. So, we introduce a fast epoch-based reclamation (EBR) algorithm called DEBRA+, which is the first lock-free EBR algorithm that can be used with trees. We also devise two techniques for accelerating lock-free data structure implementations. Using the first technique, we can efficiently eliminate dynamic allocation and reclamation of descriptors in SCX, and a large class of other algorithms. Using the second, we exploit hardware transactional memory support in modern processors to produce accelerated implementations of the template.
Applications of algebraic microlocal analysis in symplectic geometry and representation theory
Keywords: A-packet
Duistermaat
hyperfunction
Langlands
representation theory

This thesis investigates applications of microlocal geometry in both representation theory and symplectic geometry. Accordingly, there are two bodies of work contained herein. The first part of this thesis investigates a conjectural geometrization of local Arthur packets. These packets of representations of a p-adic group were invented by Arthur for the purpose of classifying the automorphic discrete spectrum of special orthogonal and symplectic groups. While their existence has been established, an explicit construction of local Arthur packets remains difficult. In the case of real groups, Adams, Barbasch, and Vogan showed how one can use a geometrization of the local Langlands correspondence to construct packets of equivariant D-modules that satisfy similar endoscopic transfer properties as the ones defining Arthur packets. We classify the contents of these “microlocal” packets in the analogue of these varieties for p-adic groups, under certain restrictions, for a plethora of split classical groups. The goal of the second part of this thesis is to find a way to make sense of the Duistermaat-Heckman function for a Hamiltonian action of a compact torus on an infinite dimensional symplectic manifold. We show that the Duistermaat-Heckman theorem can be understood in the language of hyperfunction theory, then apply this generalization to study the Hamiltonian T x S 1 action on ΩSU(2). The essential reason for introducing hyperfunction theory is that the local contribution to the Duistermaat-Heckman polynomial near the image of a fixed point is a Green's function for an infinite order differential equation. Since infinite order differential operators do not act on Schwarz distributions, we are forced to use this more general theory.