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Network-Wide Monitoring and Debugging
No full textModern networks can encompass over 100,000 servers. Managing such an extensive network with a diverse set of network policies has become more complicated with the introduction of programmable hardwares and distributed network functions. Furthermore, service level agreements (SLAs) require operators to maintain high performance and availability with low latencies. Therefore, it is crucial for operators to resolve any issues in networks quickly. The problems can occur at any layer of stack: network (load imbalance), data-plane (incorrect packet processing), control-plane (bugs in configuration) and the coordination among them. Unfortunately, existing debugging tools are not sufficient to monitor, analyze, or debug modern networks; either they lack visibility in the network, require manual analysis, or cannot check for some properties. These limitations arise from the outdated view of the networks, i.e., that we can look at a single component in isolation. In this thesis, we describe a new approach that looks at measuring, understanding, and debugging the network across devices and time. We also target modern stateful packet processing devices: programmable data-planes and distributed network functions as these becoming increasingly common part of the network. Our key insight is to leverage both in-network packet processing (to collect precise measurements) and out-of-network processing (to coordinate measurements and scale analytics). The resulting systems we design based on this approach can support testing and monitoring at the data center scale, and can handle stateful data in the network. We automate the collection and analysis of measurement data to save operator time and take a step towards self driving networks
The Early Bird Conveys The Culture: The Effects Of Preboarding Practices On Newcomer Socialization And Performance
No full textIt is widely accepted that organizations should begin socializing newcomers into the culture after their job start date. Considerable research has made it clear that onboarding newcomers during the encounter stage enhances their adjustment and performance. However, scholars have paid little attention to the impact of commencing cultural socialization earlier during what I designate as the prelude stage—the latter part of the anticipation stage, namely the time period between accepting a job offer and joining the organization. In this dissertation, I build on theories of imprinting and boundary crossing to propose that preboarding newcomers during the prelude stage—when they are more susceptible to lasting influence—may yield greater benefits for their cultural socialization and performance over time. In a field study, I measured the extent to which managers preboarded their newcomers during the prelude stage, finding that preboarding practices predicted job performance among newcomers even after controlling for realistic job previews during the anticipation stage. This relationship was mediated by newcomers’ cultural learning and adaptation. In a field experiment, randomly assigning managers to commence cultural socialization during the prelude rather than encounter stage enhanced newcomers’ job performance. This effect was mediated by newcomers’ cultural learning and adaptation, and enhanced for newcomers with lower initial affective commitment to the organization. Exploratory analyses reveal that naturally-occurring and experimentally-induced preboarding practices benefitted newcomer retention twelve months later. My theory and findings enrich knowledge about socialization, organizational culture, and managerial effectiveness by expanding the temporal boundaries around when socialization happens
The Black Worker And The Knowledge Economy In Philadelphia: University-Led Displacement Vs. Homeowner Democracy
Get PDFThis dissertation investigates the consequences of university-driven development in Philadelphia, especially for the African American communities that surround the University of Pennsylvania, Temple University, and Drexel University. It uses the theoretical contributions of W.E.B. Du Bois and David Harvey to conceptualize Philadelphia’s high rate of low-income homeownership as a product of the struggle of black workers and communities for democracy and the Right to the City. Thirty-three qualitative interviews with long-time residents, political activists, university administrators, and community institutions were conducted. Quantitative analysis including logistic regression analysis of Home Mortgage Disclosure Act (HMDA) data comparing outcomes in gentrifying and non-gentrifying neighborhoods and spatial K-cluster analysis were also conducted. Results show that university-driven development is leading to the conversion of single-family homes into apartment buildings and multifamily rentals, and a vision of the city in which developers, city officials, and university administrators wish to (in the words of one interviewee) “bring Manhattan to Philadelphia”. For homeowners, density is a shorthand for social, economic, and political displacement of the black working class and the disappearance of affordable homeownership opportunities. Density and affordable housing—and an ideology of urbanism—as conceptualized by city planners, university officials, developers, and new residents, clash with communities’ definitions of what the urban fabric of Philadelphia should be, as well as what truly affordable housing looks like. Furthermore, the influx of a student and professional population and its definition of progressivism has led to the political displacement of constituencies that have been shaped by black liberation movements. Resistance to university-driven development, whether it is the movement against the building of Temple’s Stadium, or the drive to “save-zone” neighborhoods by rezoning them from mixed residential to single family, are led by black homeowners to preserve homeownership and black electorates. They are rooted in the historic struggles of the black worker in Philadelphia. I conclude with a discussion of the context of decreasing rates of homeownership in the country as a threat to a truly democratic society
Barriers To Entry For Black Pre-Service Teachers
Get PDFFor decades, calls for an increase in the number of minority teachers have led local, state, and federal policy conversations. However, specific barriers to entry into the teaching profession for Black pre-service teachers have received less attention. Moreover, the minimally existing research on the topic is mixed. Despite being the most affected by barriers to entry into the teaching profession, little research has investigated how barriers specifically impact Black pre-service teachers during the teacher training or Educator Preparation Provider (EPP) process. This study examines one possible cause: licensure exams, of which Black test-takers have had the lowest pass rates of all racial or ethnic demographic groups since the inception of the exam.
First, this study will thoroughly review existing literature on the various theoretical barriers to entry for Black pre-service teachers, including coursework, field experiences, and licensure exams. Next, the impact of a licensure exam policy change on Black test taker pass rates in Arkansas will be assessed using various descriptive data and the difference-in-differences estimator methodology. This study hypothesizes a change in licensure exam type has negatively impacted the Black teacher workforce in Arkansas, specifically elementary school teachers. Finally, recommendations for state and federal policy and practice will be discussed
Pedagogies Of Power: Gender, Race, And Bridewell Hospital On The Early Modern Stage
No full textThis project reassesses the political configuration of poor women’s agency in early modern English culture. It examines the dramatic representation of women whose virtue was always in question: poor women, low-rank maidservants, sex workers, and “masterless women.” It argues that literary representations of poor women’s agency illuminate the gendered, racial, and colonial logics of England’s socio-political hierarchies. Playwrights depict poor women as political actors whose compliance with the dominant order is neither natural nor given but taught through shame, criminalization, and sexual violence. This project focuses on one material space where such pedagogy takes shape: London’s Bridewell Hospital, a workhouse, prison, and charitable institution founded to reform “lewd” women. When “Bridewell” is staged as a setting, I suggest that it is not just a backdrop but a political filter atop the action of the scene that freezes the play and the woman’s expression of agency in place and thus exposes how this configuration of identity and agency is useful to the national order. Understanding “Bridewell” as a cultural shorthand for the mutual constitution of racial, class, and sexual hierarchies, this project proposes new ways of reading marginalized female characters in Renaissance drama—not as minor comic characters but as political actors who knowingly negotiate sexual and racial dimensions of the social order
A Materials Study Of Topological Insulators And Two-Dimensional Ferromagnets
Get PDFTopological insulators and two-dimensional ferromagnetic materials are novel phases with wideranging applications including quantum computing, spintronics, and other advanced electronic devices with the potential for ultrathin and ultralow-power wearables. TEM, AFM, EDS, Ramanspectroscopy, and low-temperature transport measurement are used to characterize an unusual superconducting alloy formed between palladium and bismuth selenide under low-temperature annealing. TEM, AFM, and EDS are used to perform a materials study of metallic nickel and niobium annealed with Bi2Se3 under similar conditions, with the conclusion that Ni reacts to form a diffuse layer within Bi2Se3 flakes that travels along edges and line defects, and Nb does not react at all. Detailed materials analysis of Bi2Se3 flakes nanosculpted with a gallium focused ion beam and with a TEM beam is also presented, with the result that FIB ablation causes the formation of a debris field alongside the edges of a cut region, but electron-beam ablation does not. Finally, a materials, defect, and degradation study of electrochemically exfoliated ultrathin vanadium selenide nanoflakes is presented, in which the VSe2 is characterized by Raman spectroscopy and a newly-invented MFM technique inforporating torsional resonance oscillation, as well as time studies of AFM, MFM, and low-temperature TEM to investigate the effects of both air and electrolyte exposure. It is found that propylene carbonate exposure causes the breakdown of VSe2 into its elemental constituents and that passivation with dilute perfluorodecane thiol confers a concentration-dependent protective effect. This research lays the groundwork for exciting future studies into the nature and properties of Group X alloys of Bi2Se3 and potentially novel spin textures and transport characteristics of VSe2 heterostructures
Defining The Landscape Of Rare Inherited And De Novo Germline Structural Variation In Neuroblastoma
No full textNeuroblastoma is a deadly cancer of the developing sympathetic nervous system with complex genetic influences. Linkage analysis, genome-wide association studies (GWAS), next-generation sequencing, and other approaches have demonstrated that both common and rare germline variants confer risk for neuroblastoma. Nevertheless, the role of rare germline structural variants (SVs), a broad class of variants that affect more than 50 base pairs, remained undefined. This dissertation addressed this knowledge gap through three studies. First, we conducted an unbiased GWAS of large (\u3e500 kb), rare germline copy number variants (CNVs) in 5,585 neuroblastoma patients and 23,505 controls. We identified a 550-kb deletion on chromosome 16p11.2 that substantially increases risk for neuroblastoma (p=3.34x10-9, odds ratio=13.9, 95% confidence interval=5.8–33.4). Notably, 16p11.2 microdeletion has previously been associated with diverse phenotypes including autism spectrum disorder. Consistently decreased gene expression and absence of a clear second hit in matched tumors suggested multi-gene haploinsufficiency as a likely mechanism. Finally, 16p11.2 deletion arose de novo on the maternal haplotype in three patients for whom heritability could be ascertained. In a second study, we analyzed known neuroblastoma-associated genes in the same rare CNV cohort and identified three patients carrying ultra-rare germline deletions in BARD1, which were completely absent from control populations. Functional analysis of heterozygous BARD1 loss-of-function sequence variants in neuroblastoma cellular models revealed decreased BARD1 expression, widespread genomic instability, and DNA repair deficiency, suggesting that BARD1 mutations induce haploinsufficiency in neuroblastoma in the absence of a second hit. In a third study, we profiled germline SVs in whole-genome sequencing from 556 neuroblastoma patients and their parents. We identified 13 mostly-inherited candidate pathogenic SVs in known cancer predisposition genes, of which seven were highly likely to induce loss-of-function, such as a 700-kb deletion of the entire PHOX2B coding sequence, a 2-kb in-frame deletion abrogating the FHA domain of CHEK2, and a 5-kb frameshift-inducing duplication in FANCA. Altogether, this dissertation demonstrated that neuroblastoma patients harbor rare, pathogenic germline SVs that influence tumor phenotype. These findings advance biological understanding of neuroblastoma and inform genetic testing and treatment strategy
Sublinear Algorithm and Lower Bound for Combinatorial Problems
No full textAs the scale of the problems we want to solve in real life becomes larger, the input sizes of the problems we want to solve could be much larger than the memory of a single computer. In these cases, the classical algorithms may no longer be feasible options, even when they run in linear time and linear space, as the input size is too large. In this thesis, we study various combinatorial problems in different computation models that process large input sizes using limited resources. In particular, we consider the query model, streaming model, and massively parallel computation model. In addition, we also study the tradeoffs between the adaptivity and performance of algorithms in these models. We first consider two graph problems, vertex coloring problem and metric traveling salesman problem (TSP). The main results are structure results for these problems, which give frameworks for achieving sublinear algorithms of these problems in different models. We also show that the sublinear algorithms for (∆ + 1)-coloring problem are tight. We then consider the graph sparsification problem, which is an important technique for designing sublinear algorithms. We give proof of the existence of a linear size hypergraph cut sparsifier, along with a polynomial algorithm that calculates one. We also consider sublinear algorithms for this problem in the streaming and query models. Finally, we study the round complexity of submodular function minimization (SFM). In particular, we give a polynomial lower bound on the number of rounds we need to compute s − t max flow - a special case of SFM - in the streaming model. We also prove a polynomial lower bound on the number of rounds we need to solve the general SFM problem in polynomial queries
Racialized Patterns of Inequality in United States Birth Outcomes, 1990-2018
No full textLow birthweight is a pernicious public health problem that has seen little to no improvement in the United States for over 50 years. Being born low birth weight carries an increased risk of a broad range of adverse health and development outcomes and has been identified as a likely mechanism through which health and socioeconomic inequality is reproduced across generations. Racial disparities in birth weight are particularly stark. However, despite considerable attention to the issue, existing research fails to fully explain the social, institutional, and historical processes that operate to uphold racialized inequality in adverse birth outcomes. In light of recent declines in average birth weight and increases in pre-term births over recent decades, this puzzle is of particular importance to the public health and medical community, as well as to the racially minoritized populations affected by these shifts. The current dissertation approaches the problem from three different angles to better understand how racialized patterns in birth weight inequality are shaped via 1) vast shifts in the timing and level of participation in the institutions of marriage and education over time and the associated implications for racialized age patterns of low birth weight risk; 2) rapid increases in the use of obstetric interventions that have had widespread implications for the distribution of births by gestational age; and 3) the dilution of Black voting power via racialized disenfranchisement. Using standard regression techniques, classic demographic life table methods, and decomposition techniques, this dissertation finds that racialized disparities in educational attainment, exposure to obstetric intervention, and political exclusion all operate to exacerbate and/or maintain long-standing disparities in birth weight risk for racially minoritized populations. Implications of this work for future research and policy call for increased attention to the institutional and historical processes that produce racialized patterns of risk for adverse birth outcomes in Black communities
Ethical Machine Learning: Fairness, Privacy, and the Right to Be Forgotten
No full textLarge-scale algorithmic decision making has increasingly run afoul of various social norms, laws, and regulations. A prominent concern is when a learned model exhibits discrimination against some demographic group, perhaps based on race or gender. Concerns over such algorithmic discrimination have led to a recent flurry of research on fairness in machine learning, which includes new tools for designing fair models, and studies the tradeoffs between predictive accuracy and fairness. We address algorithmic challenges in this domain. Preserving privacy of data when performing analysis on it is not only a basic right for users, but it is also required by laws and regulations. How should one preserve privacy? After about two decades of fruitful research in this domain, differential privacy (DP) is considered by many the gold standard notion of data privacy. We focus on how differential privacy can be useful beyond preserving data privacy. In particular, we study the connection between differential privacy and adaptive data analysis. Users voluntarily provide huge amounts of personal data to businesses such as Facebook, Google, and Amazon, in exchange for useful services. But a basic principle of data autonomy asserts that users should be able to revoke access to their data if they no longer find the exchange of data for services worthwhile. The right for users to request the erasure of personal data appears in regulations such as the Right to be Forgotten of General Data Protection Regulation (GDPR), and the California Consumer Privacy Act (CCPA). We provide algorithmic solutions to the the problem of removing the influence of data points from machine learning models