Np dissertation summary

Specifically, we adopt cost-effectiveness as a desideratum of mechanism selection and introduce a novel approach that leverages sequential Bayesian updating to help students better allocate their attention to a subset of schools at which they have a higher probability of admission.

Using China's gaokao market as a case study, we compare the performance of this Bayesian recommendation mechanism against that of the Chinese Parallel Binning mechanism as deployed in Zhejiang in Without sacrificing outcome stability, we find that our Bayesian approach generates significant welfare gains, across all levels of preference correlation, by drastically reducing the number of schools to which students must apply.

Online 6. Developing an understanding and intuition for the underlying physical mechanisms that result in conduction in dielectrics is a challenging task to undertake experimentally, since the electron transport mechanisms are stochastic and unpredictable. In this work, we use Ginestra, a commercial tool developed by MDLSoft acquired by Applied Materials in March to simulate and provide explanations for anomalies that were observed in experimental data. We find that Ginestra is well-suited for studying metal-insulator-metal MIM device structures, providing key insights into how the endurance and reliability of RRAM cells can be optimized.

Our analysis of nonfilamentary RRAMs for Neuromorphic Computing suggests that a hybrid pulsing scheme, comprised of a stepped pulsing scheme for potentiation LTP , and a standard uniform pulsing scheme for depression LTD produces a synaptic signature that has the linearity and symmetry that is desired for neuromorphic computing applications. Online 7. This led me to analyze the history of race in television as well as the tactics of more recent shows with the hope of determining how using humor as a crutch consistently impacts the perpetuation of stereotypes, the development of diverse characters, and the representation of authentic racial experiences.

Online 8.

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We report on the modeling of the electromagnetic properties of a dark matter detector designed to look for field-like dark matter. Hidden photons are massive U 1 vectors, and are attractive cold dark matter candidates. They exhibit a weak coupling to photons through kinetic mixing, allowing them to interact with Standard Model particles. This allows the presence of a background hidden photon field to be detected by using an electromagnetic resonator. This is the motivation behind Dark Matter Radio DM Radio , an experiment that uses such a resonator to search for these particles. We present the results of modeling this resonator using finite element methods FEM.

These are techniques to numerically solve PDEs in systems that do not exhibit high degrees of symmetry. These techniques are implemented in COMSOL, which is a software that is industry standard in performing finite element analyses. We present the results of modeling the background hidden photon field as an effective current density, and measuring the observable electromagnetic fields sourced by this current density. We also study the screening current that develops in the superconducting resonator due to these fields, which is the signal that is measured. Finally, we present various measurements of the electromagnetic properties of the resonator We report on the modeling of the electromagnetic properties of a dark matter detector designed to look for field-like dark matter.

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Finally, we present various measurements of the electromagnetic properties of the resonator. Online 9. North Korea and International Cooperation on the Environment [].

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Conventional wisdom suggests that North Korea is an isolated state, with Pyongyang typically disengaged or excluded from various forms of international cooperation. Motivated by this apparent contradiction, this thesis asks: does North Korea actually comply with the MEAs it ratifies?

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If so, why? If not, why not? This thesis finds support for the notion that North Korea complies with MEAs when doing so is consistent with buttressing the stability of the Kim regime, which offers important implications for future cooperative endeavors with North Korea within the international environmental domain.

Online This story examines how Senate Bill , a California transparency law that went into effect in January that made public police use-of-force and misconduct records, has helped the families of police shooting victims by giving them access to internal investigation reports and body camera footage.

Additionally, it looks at other pushes from activists and legislators in the state to increase police transparency and accountability. Ischemia reperfusion IR injury, which is characterized by the paradoxical acceleration of damage associated with reoxygenation following ischemia, is a hallmark of myocardial infarction MI and is responsible for many post-MI complications, such as heart failure. IR injury is primarily mediated through signaling cascades that converge at mitochondria.

However, developing therapeutics that target the specific mitochondrial factors that mediate IR damage has been challenging. Then live microscopy and high throughput image analysis was performed to investigated the localization of cTnI following IR.

Lastly, in silico modeling demonstrated plausible mechanisms behind cTnI-mediated mitochondrial dysfunction. War powers in Great Britain have traditionally been the exclusive domain of the executive. Parliament did not hold a substantive vote of approval for war until , when it supported regime change in Iraq. This thesis examines why parliamentary votes of approval started in and have been consistently replicated in an otherwise diverse set of political, strategic, and international legal contexts.

Specifically, a contingent event in the vote over war in Iraq, forced by a significant Labour Party rebellion, was a critical juncture that launched a process of progressive reinforcement of the perceived superior legitimacy of parliamentary war powers. Through this reinforcement, what was a contingent choice to hold a vote in has become a politically-enforced constitutional convention that has constrained later Prime Ministers to follow earlier precedent, especially during the debates on military action against Syria and ISIS. Beyond war powers, path-dependence appears to be a promising framework to analyze convention-based constitutional change Britain more broadly.

Yang, Xuan, author.


Summary Deep neural networks DNNs have been widely used to solve many modern machine intelligence problems. However, their outstanding accuracy comes at the cost of high computation complexity which limits their speed on conventional CPUs. This difficulty has encouraged researchers to create efficient DNN accelerators. Interestingly these designs use a variety of approaches, and have not converged over time. While each design states the advantages of its approach, without a comprehensive understanding of the global space, it is difficult to understand which design choices really matter.

To address this issue, this thesis shows how the space of DNN hardware accelerators can be represented as scheduling choices, including computation orders, storage orders, etc. Different DNN micro-architectures and mappings represent specific choices of loop order and hardware parallelism for computing the seven nested loops of DNNs.

This observation enables one to create a formal taxonomy of all existing dense DNN accelerators, and systematically analyze the design space, including dataflow choice. The loop transformations needed to create these hardware variants can be precisely and concisely represented by Halide's scheduling language.

By modifying the Halide compiler to generate hardware, we create a system that can fairly compare these prior DNN accelerators, and show that many different dataflows yield similar energy efficiency with good performance. As long as properly choosing the memory sizes to accommodate the efficient blocking strategy, it can ensure that most data references stay on-chip with good locality and the function units have high resource utilization. Thus, the hardware dataflow choices become less critical, but how resources are allocated, especially in the memory system, has a large impact on energy and performance.

By optimizing hardware resource allocation while keeping throughput constant, we achieve up to 4. Advancements in virtual reality VR technology have the potential to revolutionise many areas of medicine. In particular, VR use for paediatric pain management is offering promising clinical results, yet the long-term effects of this technology—both for patients and healthcare systems—remain unknown. The goal of this honors thesis was to analyse the current state of VR integration into paediatric pain management. This research answers two main questions: 1 How does VR function as an effective pain management tool for children?

This first part of this thesis addresses the former question through a comprehensive analysis of relevant literature related to VR use in children experiencing acute or chronic pain. This thesis concluded that there is a significant need for further research on the use of VR in paediatric pain management. Additionally, in order for VR to be effectively employed in current clinical practice, it is essential that preliminary provider experiences be used to develop a standard VR implementation protocol that can be used by healthcare systems across the United States.

Ultimately, this thesis contributes both to the academic literature on VR use and offers a novel analysis of provider opinions that will help to ensure that VR integration into paediatric pain management is done both with efficacy and integrity. Abstraction : a literary history []. Heuser, Ryan James, author. Summary This dissertation turns to computational semantics and natural language processing in order to trace forms of abstract language as they travel across literary history.

Abstract words, when studied at the scale of the digital archive, weave a broad historical pattern for which we have no name: a slow-moving, centuries-long rise and fall across the literature of the modern period. This pattern, invisible but to the macroscope of distant reading, provides a new kind of map, and a new kind of prominence, to the literary history of the long eighteenth century. Zooming in on these distant data, I explore two literary forms of abstract language more closely: "abstract realism, " in the "tell, don't show" narration of the early realist novel; and "abstract persons, " in the personified abstractions of the mid-eighteenth-century poetry.

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Drawing on both close reading and computational analysis, my dissertation historicizes a cultural logic of abstraction as it develops across the long eighteenth century through a range of linguistic, literary, and social forms. Critically, Abstraction: A Literary History aims to recuperate abstraction as both a method and an object of literary study.

Np dissertation summary

For decades, literary criticism has been largely hostile to abstract methods, from the New Critics' insistence on a poem's irreducible ambiguity to the new historicism's emphasis on its material circulation and exchange. My dissertation participates in, and theorizes, what I call an "abstract turn" in criticism, a set of related trends encompassing not only digital humanities work, but also newly abstract, interdisciplinary notions of form, along with new investments in large-scale critical frameworks like the institutional, the transhistorical, and the planetary.

Working, computationally, at the scale of four hundred years, Abstraction: A Literary History visualizes this abstract turn within a broader history of negotiations between the literary, the critical, and the abstract. Accelerating machine learning with training data management []. Ratner, Alexander Jason, author. Summary One of the biggest bottlenecks in developing machine learning applications today is the need for large hand-labeled training datasets. Even at the world's most sophisticated technology companies, and especially at other organizations across science, medicine, industry, and government, the time and monetary cost of labeling and managing large training datasets is often the blocking factor in using machine learning.

In this thesis, we describe work on training data management systems that enable users to programmatically build and manage training datasets, rather than labeling and managing them by hand, and present algorithms and supporting theory for automatically modeling this noisier process of training set specification in order to improve the resulting training set quality.

We then describe extensive empirical results and real-world deployments demonstrating that programmatically building, managing, and modeling training sets in this way can lead to radically faster, more flexible, and more accessible ways of developing machine learning applications. We start by describing data programming, a paradigm for labeling training datasets programmatically rather than by hand, and Snorkel, an open source training data management system built around data programming that has been used by major technology companies, academic labs, and government agencies to build machine learning applications in days or weeks rather than months or years.

In Snorkel, rather than hand-labeling training data, users write programmatic operators called labeling functions, which label data using various heuristic or weak supervision strategies such as pattern matching, distant supervision, and other models. These labeling functions can have noisy, conflicting, and correlated outputs, which Snorkel models and combines into clean training labels without requiring any ground truth using theoretically consistent modeling approaches we develop.

We then report on extensive empirical validations, user studies, and real-world applications of Snorkel in industrial, scientific, medical, and other use cases ranging from knowledge base construction from text data to medical monitoring over image and video data. Next, we will describe two other approaches for enabling users to programmatically build and manage training datasets, both currently integrated into the Snorkel open source framework: Snorkel MeTaL, an extension of data programming and Snorkel to the setting where users have multiple related classification tasks, in particular focusing on multi-task learning; and TANDA, a system for optimizing and managing strategies for data augmentation, a critical training dataset management technique wherein a labeled dataset is artificially expanded by transforming data points.

Finally, we will conclude by outlining future research directions for further accelerating and democratizing machine learning workflows, such as higher-level programmatic interfaces and massively multi-task frameworks. Park, Seo Jin, author. Summary Today's datacenter applications demand large-scale and low-latency systems.

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