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Over the course of his career, the artist Rembrandt van Rijn produced eight prints depicting St. Jerome, a fourth-century ascetic and theologian. In this thesis, I examine why Rembrandt chose Jerome to represent so often in print and in what ways the saint’s imagery and iconography was adapted to reflect seventeenth-century Dutch culture and values. Rembrandt’s prints of Jerome can be divided into two even groups, with four etchings of the saint as repentant sinner and four of the saint as scholar. In the prints of the repentant Jerome, I argue that Rembrandt focuses on the act of prayer and quiet contemplation to align with Protestant ideas of penitence. In this way, Rembrandt reflects a Protestant version of asceticism that could be used to model prayer and penitence in the daily life of Dutch citizens worried for the fate of their souls. One of these four prints of Jerome in penitence was produced in collaboration with a printmaker, Jan van Vliet. With this print, I analyze the practical reasons surrounding Rembrandt’s production and posit that his collaboration with Van Vliet broadly and the prints of Jerome specifically were meant to advertise his skill, attract patrons, and attain success on the market. I lastly discuss Rembrandt’s four prints of Jerome as scholar and view them through the lens of Christian humanism and intellectual culture prevalent in the Dutch Republic during this time. Jerome’s status as patron saint of scholars and advocate for the study of languages and primacy of the original Word resonated with Protestant thinkers of the early modern era. The characterization of Jerome’s scholarship again aligned with a newly developing form of asceticism that focused on virtue and engagement with God. This thesis argues that all eight prints of Jerome in his dual guises as sinner and scholar reveal Rembrandt’s construction of the saint as an ideal of religious devotion. In these prints, Jerome’s penitence and scholarship both provide a model for the path to virtue as believers sought a way to be reassured of their salvation and lead a life of pious humility.

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For the optimization of production in an operating marginal oil field, it is necessary to consider the reservoir inflow, the artificial lift systems, as well as the surface facilities. Since most reservoir simulation software does not include detailed facility modeling, an integrated model of an entire field has been developed including the surface facilities, to allow detailed modeling of the entire field operation. This model is useful for optimizing production and for use in field surveillance activities, as well as investigating the applicability of simplified engineering assumptions

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Andre and Quillen introduced a (co)homology theory for augmented commutative rings. Strickland [31] initially proposed some issues with the analogue of the abelianization functor in the equivariant setting. These were resolved by Hill [15] who further gave the notion of a genuine derivation and a module of Kähler differentials. We build on this endeavor by expanding to incomplete Tambara functors, introducing the cotangent complex and its various properties, and producing an analogue of the fundamental spectral sequence.

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Statistical inference, such as hypothesis testing and calculating a confidence interval, is an important tool for accessing uncertainty in machine learning and statistical problems. Stochastic gradient methods, such as stochastic gradient descent (SGD), have recently been successfully applied to point estimation in large scale machine learning problems. In this work, we present novel stochastic gradient methods for statistical inference in large scale machine learning problems. Unregularized M -estimation using SGD. Using SGD with a fixed step size, we demonstrate that the average of such SGD sequences can be used for statistical inference, after proper scaling. An intuitive analysis using the Ornstein-Uhlenbeck process suggests that such averages are asymptotically normal. From a practical perspective, our SGD-based inference procedure is a first order method, and is well-suited for large scale problems. To show its merits, we apply it to both synthetic and real datasets, and demonstrate that its accuracy is comparable to classical statistical methods, while requiring potentially far less computation. Approximate Newton-based statistical inference using only stochastic gradients for unregularized M -estimation. We present a novel inference framework for convex empirical risk minimization, using approximate stochastic Newton steps. The proposed algorithm is based on the notion of finite differences and allows the approximation of a Hessian-vector product from first-order information. In theory, our method efficiently computes the statistical error covariance in M -estimation for unregularized convex learning problems, without using exact second order information, or resampling the entire data set. In practice, we demonstrate the effectiveness of our framework on large-scale machine learning problems, that go even beyond convexity: as a highlight, our work can be used to detect certain adversarial attacks on neural networks. High dimensional linear regression statistical inference using only stochastic gra- dients. As an extension of the approximate Newton-based statistical inference algorithm for unregularized problems, we present a similar algorithm, using only stochastic gradients, for statistical inference in high dimensional linear regression, where the number of features is much larger than the number of samples. Stochastic gradient methods for time series analysis. We present a novel stochastic gradient descent algorithm for time series analysis, which correctly captures correlation structures in a time series dataset during optimization. Instead of uniformly sampling indices in vanilla SGD, we uniformly sample contiguous blocks of indices, where the block length depends on the dataset.

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River deltas are densely populated and dynamically changing environments located at the boundary between land and sea. Population demands for water as well as rising sea levels are increasingly threatening aquifer water quality in deltaic regions. The rate at which aquifer contamination by salt water or other contaminants occurs is dictated, in part, by the arrangement of sediment within the subsurface. In this work, we examine the heterogeneity of the subsurface from a structural vantage to better understand how surface processes and geometry are linked to subsurface architecture. The numerical model, DeltaRCM, is applied to simulate delta evolution under a variety of input conditions. The resulting model outputs simulate 800 years during which the growing delta generates a subsurface volume that is over 40m deep. Surface channel properties and behavior, such as channel depths and channel planform decay rates are measured. Similarly, the structure of the sand bodies in the subsurface domain is evaluated. These different types of analyses, surface and subsurface, are ultimately compared to take a first-look at how channel properties in a deltaic environment may relate to subsurface structure and form. Broadly, expectations about channel trends and subsurface structure from the field of geomorphology are supported. Channel depths decrease with distance from the inlet, and as the input sand proportion increases. Similarly, the channelized fraction of the delta surface increases with higher input sand fraction values. In the subsurface, different types of channel behavior on the surface correspond to different structures. The sand bodies are larger when the surface channels are shallower and more mobile. In addition, the spatial continuity within strike sections (sections taken perpendicular to the inlet channel) increases with channel depth. Comparisons of the modeled subsurface with stochastically re-arranged replicates have confirmed the assertion that surface processes create unique subsurface structures. When the input proportion of sediment contains at least 40% sand by volume, the average size of the subsurface sand bodies follows a power-law relation with respect to surface channel depths and the average channelized fraction of the delta platform. The range of spatial entropy (disorder) also increases with channel depth. Within models, with increasing distance from the inlet both channel depths and spatial entropy ranges decrease. Changing the input sediment proportions over the course of the delta evolution provides mixed results. Some channel parameters like channel depth are indistinguishable from steady input cases, while others are influenced by the initial topographic setup. In the subsurface, variable sediment input proportions create vastly different sand body geometries depending on the rate of variation of the input sand proportion. When the input sand proportion is gradually increased, the average sand body size becomes very large; however when the sand input is abruptly increased, the mean sand body value is less than a steady sand input analog.

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Aria for contrabass and piano / Tetsuo Kawakami -- Sonata for double bass and piano / Paul Hindemith -- A Carmen fantasy for double bass and piano / Frank Proto

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The traffic assignment problem (TAP) represents the final and most computationally difficult step in the Urban Transportation Modeling System (UTMS). Prior work has studied several means of solving this problem quickly. This thesis furthers our understanding of this problem by investigating two methods designed to speed up TAP solution scenarios. A free and open-source implementation of the UTMS was implemented from scratch, including an implementation of Dial’s Algorithm B to solve the TAP. Chapter II of this thesis investigates the effects on computation time of a single Algorithm B parameter: the number of equilibrations performed on solution bushes prior to improvement. The results show that determining the best-performing value for this parameter is network-dependent, and that a point exists beyond which additional equilibrations do not provide improvements in runtime, potentially slowing down some networks’ computation. Chapter III investigates methods of approximating TAP solutions using artificial neural networks (ANNs) in the context of the network design problem, in which multiple different network designs may be tested. To remove the need to re-solve the TAP under each scenario, an ANN is trained to predict link flows given changes in network capacity. The results show that this method provides close approximations to the analytical solution in substantially less time than evaluating all possible scenarios from scratch.

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Database users face a tension between ease-of-programming and high performance: ACID transactions can greatly simplify the programming effort of database applications by providing four useful properties—atomicity, consistency, isolation, and durability, but enforcing these properties can degrade performance. This dissertation eases this tension by improving the performance of ACID transactions for scenarios where data contention is the bottleneck. The approach that we take is federating concurrency control (CC) mechanisms. It is based on the observation that any single CC mechanism is bound to make trade-offs that cause it to perform well in some cases but poorly in others. A federation opens the opportunity of applying each mechanism only to the set of transactions or workloads where it shines, while maintaining isolation. In particular, this work builds upon Modular Concurrency Control (MCC), a recent technique that federates CCs by partitioning transactions into groups, and by applying different CC mechanisms in each group. This dissertation addresses two critical shortcomings in the current embodiment of MCC. First, cross-group data conflicts are handled with a single, unoptimized CC mechanism that can significantly limit performance. Second, configuring MCC is a complex task, which runs counter to MCC’s purpose: to improve performance without sacrificing ease-of-programming. To address these problems, this dissertation presents Tebaldi, a new transactional database that brings Modular Concurrency Control to the next level, both figuratively and literally. Tebaldi introduces a new, hierarchical model to MCC that partitions transactions recursively to compose CC mechanisms in a multi-level tree. This model increases flexibility in federating CC mechanisms, which is the key to realizing the performance potential of federation. Tebaldi reduces configuration complexity by managing the MCC federation automatically: it can detect performance issues in the current workload in real-time, and automatically adjusts its configuration to improve its performance.

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Sonatine modale : pour flute et clarinette, op. 155 / Charles Koechlin -- Histoire du tango / Astor Piazzolla -- Sonata for flute, viola and harp, L. 137 / Claude Debussy -- Assobio a jato = The jet whistle / Heitor Villa-Lobos.

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Sexual debut is defined as the first time an individual engages in penetrative sexual intercourse. This experience, if negative, has been associated with adverse outcomes such as substance abuse, delinquency, depressive symptoms, and negative self-schemas. The purpose of this study was to expand the literature on sexual debut by exploring specific aspects of sexual debut (ie. age at sexual debut, and nonconsensual or consensual sexual debut) and their relationship with eating attitudes and behaviors. Participants (N=448) completed a single session online survey that measured sexual debut, self-esteem, body satisfaction, body esteem and eating attitudes and behaviors. A nonconsensual sexual encounter at sexual debut significantly predicted higher levels of disordered eating attitudes and behaviors. This relationship was mediated by variables of self-esteem and body satisfaction. Understanding sexual debut as a contributor to disordered eating and other variables that affect this relationship can be valuable in expanding the literature on the development of disordered eating as well as women’s sexual health and well-being.

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